import math
import numpy as np
from typing import Optional, Tuple, List, Dict, Any
from packaging.version import Version
from deepmd.env import tf
from deepmd.common import get_activation_func, get_precision, cast_precision
from deepmd.env import GLOBAL_TF_FLOAT_PRECISION
from deepmd.env import TF_VERSION
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.network import one_layer, embedding_net, embedding_net_rand_seed_shift
from deepmd.utils.tabulate import DPTabulate
from deepmd.utils.type_embed import embed_atom_type
from deepmd.utils.sess import run_sess
from deepmd.utils.graph import load_graph_def, get_tensor_by_name_from_graph, get_tensor_by_name
from deepmd.utils.graph import get_attention_layer_variables_from_graph_def
from deepmd.utils.errors import GraphWithoutTensorError
from .descriptor import Descriptor
from .se_a import DescrptSeA
[docs]@Descriptor.register("se_atten")
class DescrptSeAtten(DescrptSeA):
"""
Parameters
----------
rcut
The cut-off radius :math:`r_c`
rcut_smth
From where the environment matrix should be smoothed :math:`r_s`
sel : list[str]
sel[i] specifies the maxmum number of type i atoms in the cut-off radius
neuron : list[int]
Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}`
axis_neuron
Number of the axis neuron :math:`M_2` (number of columns of the sub-matrix of the embedding matrix)
resnet_dt
Time-step `dt` in the resnet construction:
y = x + dt * \\phi (Wx + b)
trainable
If the weights of embedding net are trainable.
seed
Random seed for initializing the network parameters.
type_one_side
Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
exclude_types : List[List[int]]
The excluded pairs of types which have no interaction with each other.
For example, `[[0, 1]]` means no interaction between type 0 and type 1.
set_davg_zero
Set the shift of embedding net input to zero.
activation_function
The activation function in the embedding net. Supported options are |ACTIVATION_FN|
precision
The precision of the embedding net parameters. Supported options are |PRECISION|
uniform_seed
Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
attn
The length of hidden vector during scale-dot attention computation.
attn_layer
The number of layers in attention mechanism.
attn_dotr
Whether to dot the relative coordinates on the attention weights as a gated scheme.
attn_mask
Whether to mask the diagonal in the attention weights.
multi_task
If the model has multi fitting nets to train.
"""
def __init__(self,
rcut: float,
rcut_smth: float,
sel: int,
ntypes: int,
neuron: List[int] = [24, 48, 96],
axis_neuron: int = 8,
resnet_dt: bool = False,
trainable: bool = True,
seed: Optional[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,
attn: int = 128,
attn_layer: int = 2,
attn_dotr: bool = True,
attn_mask: bool = False,
multi_task: bool = False
) -> None:
DescrptSeA.__init__(self,
rcut,
rcut_smth,
[sel],
neuron=neuron,
axis_neuron=axis_neuron,
resnet_dt=resnet_dt,
trainable=trainable,
seed=seed,
type_one_side=type_one_side,
exclude_types=exclude_types,
set_davg_zero=set_davg_zero,
activation_function=activation_function,
precision=precision,
uniform_seed=uniform_seed,
multi_task=multi_task
)
"""
Constructor
"""
assert (Version(TF_VERSION) > Version('2')), "se_atten only support tensorflow version 2.0 or higher."
self.ntypes = ntypes
self.att_n = attn
self.attn_layer = attn_layer
self.attn_mask = attn_mask
self.attn_dotr = attn_dotr
# descrpt config
self.sel_all_a = [sel]
self.sel_all_r = [0]
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)
self.beta = np.zeros([self.attn_layer, self.filter_neuron[-1]]).astype(GLOBAL_NP_FLOAT_PRECISION)
self.gamma = np.ones([self.attn_layer, self.filter_neuron[-1]]).astype(GLOBAL_NP_FLOAT_PRECISION)
self.attention_layer_variables = None
sub_graph = tf.Graph()
with sub_graph.as_default():
name_pfx = 'd_sea_'
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, self.descrpt_deriv_t, self.rij_t, self.nlist_t, self.nei_type_vec_t, self.nmask_t \
= op_module.prod_env_mat_a_mix(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,
rcut_r_smth=self.rcut_r_smth,
sel_a=self.sel_all_a,
sel_r=self.sel_all_r)
self.sub_sess = tf.Session(graph=sub_graph, config=default_tf_session_config)
[docs] def build(self,
coord_: tf.Tensor,
atype_: tf.Tensor,
natoms: tf.Tensor,
box_: tf.Tensor,
mesh: tf.Tensor,
input_dict: dict,
reuse: Optional[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)
t_ndescrpt = tf.constant(self.ndescrpt,
name='ndescrpt',
dtype=tf.int32)
t_sel = tf.constant(self.sel_a,
name='sel',
dtype=tf.int32)
t_original_sel = tf.constant(self.original_sel if self.original_sel is not None else self.sel_a,
name='original_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))
with tf.control_dependencies([t_sel, t_original_sel]):
coord = tf.reshape(coord_, [-1, natoms[1] * 3])
box = tf.reshape(box_, [-1, 9])
atype = tf.reshape(atype_, [-1, natoms[1]])
self.attn_weight = [None for i in range(self.attn_layer)]
self.angular_weight = [None for i in range(self.attn_layer)]
self.attn_weight_final = [None for i in range(self.attn_layer)]
self.descrpt, self.descrpt_deriv, self.rij, self.nlist, self.nei_type_vec, self.nmask \
= op_module.prod_env_mat_a_mix(coord,
atype,
natoms,
box,
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_all_a,
sel_r=self.sel_all_r)
self.nei_type_vec = tf.reshape(self.nei_type_vec, [-1])
self.nmask = tf.cast(tf.reshape(self.nmask, [-1, 1, self.sel_all_a[0]]), GLOBAL_TF_FLOAT_PRECISION)
self.negative_mask = -(2 << 32) * (1.0 - self.nmask)
# 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.descrpt_reshape = tf.reshape(self.descrpt, [-1, self.ndescrpt])
self.atype_nloc = tf.reshape(tf.slice(atype, [0, 0], [-1, natoms[0]]),
[-1]) ## lammps will have error without this
self._identity_tensors(suffix=suffix)
self.dout, self.qmat = self._pass_filter(self.descrpt_reshape,
self.atype_nloc,
natoms,
input_dict,
suffix=suffix,
reuse=reuse,
trainable=self.trainable)
# only used when tensorboard was set as true
tf.summary.histogram('embedding_net_output', self.dout)
return self.dout
def _pass_filter(self,
inputs,
atype,
natoms,
input_dict,
reuse=None,
suffix='',
trainable=True):
assert (input_dict is not None and input_dict.get('type_embedding', None) is not None), \
'se_atten desctiptor must use type_embedding'
type_embedding = input_dict.get('type_embedding', None)
inputs = tf.reshape(inputs, [-1, natoms[0], self.ndescrpt])
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, natoms, name='filter_type_all' + suffix, suffix=suffix,
reuse=reuse, trainable=trainable, activation_fn=self.filter_activation_fn,
type_embedding=type_embedding, atype=atype)
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 _compute_dstats_sys_smth(self,
data_coord,
data_box,
data_atype,
natoms_vec,
mesh,
mixed_type=False,
real_natoms_vec=None):
dd_all, descrpt_deriv_t, rij_t, nlist_t, nei_type_vec_t, nmask_t \
= run_sess(self.sub_sess, [self.stat_descrpt, self.descrpt_deriv_t, self.rij_t, self.nlist_t, self.nei_type_vec_t, self.nmask_t],
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,
})
if mixed_type:
nframes = dd_all.shape[0]
sysr = [0. for i in range(self.ntypes)]
sysa = [0. for i in range(self.ntypes)]
sysn = [0 for i in range(self.ntypes)]
sysr2 = [0. for i in range(self.ntypes)]
sysa2 = [0. for i in range(self.ntypes)]
for ff in range(nframes):
natoms = real_natoms_vec[ff]
dd_ff = np.reshape(dd_all[ff], [-1, self.ndescrpt * natoms[0]])
start_index = 0
for type_i in range(self.ntypes):
end_index = start_index + self.ndescrpt * natoms[2 + type_i] # center atom split
dd = dd_ff[:, start_index:end_index]
dd = np.reshape(dd, [-1, self.ndescrpt]) # nframes * typen_atoms , nnei * 4
start_index = end_index
# compute
dd = np.reshape(dd, [-1, 4]) # nframes * typen_atoms * nnei, 4
ddr = dd[:, :1]
dda = dd[:, 1:]
sumr = np.sum(ddr)
suma = np.sum(dda) / 3.
sumn = dd.shape[0]
sumr2 = np.sum(np.multiply(ddr, ddr))
suma2 = np.sum(np.multiply(dda, dda)) / 3.
sysr[type_i] += sumr
sysa[type_i] += suma
sysn[type_i] += sumn
sysr2[type_i] += sumr2
sysa2[type_i] += suma2
else:
natoms = natoms_vec
dd_all = np.reshape(dd_all, [-1, self.ndescrpt * natoms[0]])
start_index = 0
sysr = []
sysa = []
sysn = []
sysr2 = []
sysa2 = []
for type_i in range(self.ntypes):
end_index = start_index + self.ndescrpt * natoms[2 + type_i] # center atom split
dd = dd_all[:, start_index:end_index]
dd = np.reshape(dd, [-1, self.ndescrpt]) # nframes * typen_atoms , nnei * 4
start_index = end_index
# compute
dd = np.reshape(dd, [-1, 4]) # nframes * typen_atoms * nnei, 4
ddr = dd[:, :1]
dda = dd[:, 1:]
sumr = np.sum(ddr)
suma = np.sum(dda) / 3.
sumn = dd.shape[0]
sumr2 = np.sum(np.multiply(ddr, ddr))
suma2 = np.sum(np.multiply(dda, dda)) / 3.
sysr.append(sumr)
sysa.append(suma)
sysn.append(sumn)
sysr2.append(sumr2)
sysa2.append(suma2)
return sysr, sysr2, sysa, sysa2, sysn
def _lookup_type_embedding(
self,
xyz_scatter,
natype,
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]
self.test_type_embedding = type_embedding
self.test_nei_embed = tf.nn.embedding_lookup(type_embedding,
self.nei_type_vec) # 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(self.test_nei_embed, [-1, te_out_dim])
self.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:
self.atm_embed = tf.nn.embedding_lookup(type_embedding, natype) # shape is [nframes*natoms[0], te_out_dim]
self.atm_embed = tf.tile(self.atm_embed,
[1, self.nnei]) # shape is [nframes*natoms[0], self.nnei*te_out_dim]
self.atm_embed = tf.reshape(self.atm_embed,
[-1, te_out_dim]) # shape is [nframes*natoms[0]*self.nnei, te_out_dim]
self.embedding_input_2 = tf.concat([self.embedding_input, self.atm_embed],
1) # shape is [nframes*natoms[0]*self.nnei, 1+te_out_dim+te_out_dim]
return self.embedding_input_2
return self.embedding_input
def _feedforward(self, input_xyz, d_in, d_mid):
residual = input_xyz
input_xyz = tf.nn.relu(one_layer(
input_xyz,
d_mid,
name='c_ffn1',
reuse=tf.AUTO_REUSE,
seed=self.seed,
activation_fn=None,
precision=self.filter_precision,
trainable=True,
uniform_seed=self.uniform_seed,
initial_variables=self.attention_layer_variables))
input_xyz = one_layer(
input_xyz,
d_in,
name='c_ffn2',
reuse=tf.AUTO_REUSE,
seed=self.seed,
activation_fn=None,
precision=self.filter_precision,
trainable=True,
uniform_seed=self.uniform_seed,
initial_variables=self.attention_layer_variables)
input_xyz += residual
input_xyz = tf.keras.layers.LayerNormalization()(input_xyz)
return input_xyz
def _scaled_dot_attn(self, Q, K, V, temperature, input_r, dotr=False, do_mask=False, layer=0, save_weights=True):
attn = tf.matmul(Q / temperature, K, transpose_b=True)
attn *= self.nmask
attn += self.negative_mask
attn = tf.nn.softmax(attn, axis=-1)
attn *= tf.reshape(self.nmask, [-1, attn.shape[-1], 1])
if save_weights:
self.attn_weight[layer] = attn[0] # atom 0
if dotr:
angular_weight = tf.matmul(input_r, input_r, transpose_b=True) # normalized
attn *= angular_weight
if save_weights:
self.angular_weight[layer] = angular_weight[0] # atom 0
self.attn_weight_final[layer] = attn[0] # atom 0
if do_mask:
nei = int(attn.shape[-1])
mask = tf.cast(tf.ones((nei, nei)) - tf.eye(nei), self.filter_precision)
attn *= mask
output = tf.matmul(attn, V)
return output
def _attention_layers(
self,
input_xyz,
layer_num,
shape_i,
outputs_size,
input_r,
dotr=False,
do_mask=False,
trainable=True,
suffix=''
):
sd_k = tf.sqrt(tf.cast(1., dtype=self.filter_precision))
for i in range(layer_num):
name = 'attention_layer_{}{}'.format(i, suffix)
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
# input_xyz_in = tf.nn.l2_normalize(input_xyz, -1)
Q_c = one_layer(
input_xyz,
self.att_n,
name='c_query',
scope=name+'/',
reuse=tf.AUTO_REUSE,
seed=self.seed,
activation_fn=None,
precision=self.filter_precision,
trainable=trainable,
uniform_seed=self.uniform_seed,
initial_variables=self.attention_layer_variables)
K_c = one_layer(
input_xyz,
self.att_n,
name='c_key',
scope=name+'/',
reuse=tf.AUTO_REUSE,
seed=self.seed,
activation_fn=None,
precision=self.filter_precision,
trainable=trainable,
uniform_seed=self.uniform_seed,
initial_variables=self.attention_layer_variables)
V_c = one_layer(
input_xyz,
self.att_n,
name='c_value',
scope=name+'/',
reuse=tf.AUTO_REUSE,
seed=self.seed,
activation_fn=None,
precision=self.filter_precision,
trainable=trainable,
uniform_seed=self.uniform_seed,
initial_variables=self.attention_layer_variables)
# # 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 att_n
Q_c = tf.nn.l2_normalize(tf.reshape(Q_c, (-1, shape_i[1] // 4, self.att_n)), -1)
K_c = tf.nn.l2_normalize(tf.reshape(K_c, (-1, shape_i[1] // 4, self.att_n)), -1)
V_c = tf.nn.l2_normalize(tf.reshape(V_c, (-1, shape_i[1] // 4, self.att_n)), -1)
input_att = self._scaled_dot_attn(Q_c, K_c, V_c, sd_k, input_r, dotr=dotr, do_mask=do_mask, layer=i)
input_att = tf.reshape(input_att, (-1, self.att_n))
# (natom x nei_type_i) x out_size
input_xyz += one_layer(
input_att,
outputs_size[-1],
name='c_out',
scope=name+'/',
reuse=tf.AUTO_REUSE,
seed=self.seed,
activation_fn=None,
precision=self.filter_precision,
trainable=trainable,
uniform_seed=self.uniform_seed,
initial_variables=self.attention_layer_variables)
input_xyz = tf.keras.layers.LayerNormalization(beta_initializer=tf.constant_initializer(self.beta[i]),
gamma_initializer=tf.constant_initializer(self.gamma[i]))(input_xyz)
# input_xyz = self._feedforward(input_xyz, outputs_size[-1], self.att_n)
return input_xyz
def _filter_lower(
self,
type_i,
type_input,
start_index,
incrs_index,
inputs,
type_embedding=None,
atype=None,
is_exclude=False,
activation_fn=None,
bavg=0.0,
stddev=1.0,
trainable=True,
suffix='',
name='filter_',
reuse=None
):
"""
input env matrix, returns R.G
"""
outputs_size = [1] + self.filter_neuron
# cut-out inputs
# with natom x (nei_type_i x 4)
inputs_i = tf.slice(inputs,
[0, start_index * 4],
[-1, incrs_index * 4])
shape_i = inputs_i.get_shape().as_list()
natom = tf.shape(inputs_i)[0]
# with (natom x nei_type_i) x 4
inputs_reshape = tf.reshape(inputs_i, [-1, 4])
# with (natom x nei_type_i) x 1
xyz_scatter = tf.reshape(tf.slice(inputs_reshape, [0, 0], [-1, 1]), [-1, 1])
assert atype is not None, 'atype must exist!!'
type_embedding = tf.cast(type_embedding, self.filter_precision)
xyz_scatter = self._lookup_type_embedding(
xyz_scatter, atype, type_embedding)
if self.compress:
raise RuntimeError('compression of attention descriptor is not supported at the moment')
# natom x 4 x outputs_size
if (not is_exclude):
with tf.variable_scope(name, reuse=reuse):
# with (natom x nei_type_i) 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,
name_suffix=suffix,
stddev=stddev,
bavg=bavg,
seed=self.seed,
trainable=trainable,
uniform_seed=self.uniform_seed,
initial_variables=self.embedding_net_variables,
mixed_prec=self.mixed_prec)
if (not self.uniform_seed) and (self.seed is not None): self.seed += self.seed_shift
input_r = tf.slice(tf.reshape(inputs_i, (-1, shape_i[1] // 4, 4)), [0, 0, 1], [-1, -1, 3])
input_r = tf.nn.l2_normalize(input_r, -1)
# natom x nei_type_i x out_size
xyz_scatter_att = tf.reshape(
self._attention_layers(xyz_scatter, self.attn_layer, shape_i, outputs_size, input_r,
dotr=self.attn_dotr, do_mask=self.attn_mask, trainable=trainable, suffix=suffix),
(-1, shape_i[1] // 4, outputs_size[-1]))
# xyz_scatter = tf.reshape(xyz_scatter, (-1, shape_i[1] // 4, outputs_size[-1]))
else:
# we can safely return the final xyz_scatter filled with zero directly
return tf.cast(tf.fill((natom, 4, outputs_size[-1]), 0.), self.filter_precision)
# When using tf.reshape(inputs_i, [-1, shape_i[1]//4, 4]) below
# [588 24] -> [588 6 4] correct
# but if sel is zero
# [588 0] -> [147 0 4] incorrect; the correct one is [588 0 4]
# So we need to explicitly assign the shape to tf.shape(inputs_i)[0] instead of -1
return tf.matmul(tf.reshape(inputs_i, [natom, shape_i[1] // 4, 4]), xyz_scatter_att, transpose_a=True)
@cast_precision
def _filter(
self,
inputs,
type_input,
natoms,
type_embedding=None,
atype=None,
activation_fn=tf.nn.tanh,
stddev=1.0,
bavg=0.0,
suffix='',
name='linear',
reuse=None,
trainable=True):
nframes = tf.shape(tf.reshape(inputs, [-1, natoms[0], self.ndescrpt]))[0]
# natom x (nei x 4)
shape = inputs.get_shape().as_list()
outputs_size = [1] + self.filter_neuron
outputs_size_2 = self.n_axis_neuron
all_excluded = all([(type_input, type_i) in self.exclude_types for type_i in range(self.ntypes)])
if all_excluded:
# all types are excluded so result and qmat should be zeros
# we can safaly return a zero matrix...
# See also https://stackoverflow.com/a/34725458/9567349
# result: natom x outputs_size x outputs_size_2
# qmat: natom x outputs_size x 3
natom = tf.shape(inputs)[0]
result = tf.cast(tf.fill((natom, outputs_size_2, outputs_size[-1]), 0.), GLOBAL_TF_FLOAT_PRECISION)
qmat = tf.cast(tf.fill((natom, outputs_size[-1], 3), 0.), GLOBAL_TF_FLOAT_PRECISION)
return result, qmat
start_index = 0
type_i = 0
# natom x 4 x outputs_size
xyz_scatter_1 = self._filter_lower(
type_i, type_input,
start_index, np.cumsum(self.sel_a)[-1],
inputs,
type_embedding=type_embedding,
is_exclude=False,
activation_fn=activation_fn,
stddev=stddev,
bavg=bavg,
trainable=trainable,
suffix=suffix,
name=name,
reuse=reuse,
atype=atype)
# 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)
if self.original_sel is None:
# shape[1] = nnei * 4
nnei = shape[1] / 4
else:
nnei = tf.cast(tf.Variable(np.sum(self.original_sel), dtype=tf.int32, trainable=False, name="nnei"),
self.filter_precision)
xyz_scatter_1 = xyz_scatter_1 / nnei
# 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_1 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
[docs] def init_variables(self,
graph: tf.Graph,
graph_def: tf.GraphDef,
suffix: str = "",
) -> None:
"""
Init the embedding net variables with the given dict
Parameters
----------
graph : tf.Graph
The input frozen model graph
graph_def : tf.GraphDef
The input frozen model graph_def
suffix : str, optional
The suffix of the scope
"""
super().init_variables(graph=graph, graph_def=graph_def, suffix=suffix)
self.attention_layer_variables = get_attention_layer_variables_from_graph_def(graph_def, suffix=suffix)
if self.attn_layer > 0:
self.beta[0] = self.attention_layer_variables['attention_layer_0{}/layer_normalization/beta'.format(suffix)]
self.gamma[0] = self.attention_layer_variables['attention_layer_0{}/layer_normalization/gamma'.format(suffix)]
for i in range(1, self.attn_layer):
self.beta[i] = self.attention_layer_variables[
'attention_layer_{}{}/layer_normalization_{}/beta'.format(i, suffix, i)]
self.gamma[i] = self.attention_layer_variables[
'attention_layer_{}{}/layer_normalization_{}/gamma'.format(i, suffix, i)]