# SPDX-License-Identifier: LGPL-3.0-or-later
from typing import (
List,
Optional,
)
import numpy as np
from deepmd.common import (
cast_precision,
get_activation_func,
get_precision,
)
from deepmd.env import (
tf,
)
from deepmd.fit.fitting import (
Fitting,
)
from deepmd.loss.loss import (
Loss,
)
from deepmd.loss.tensor import (
TensorLoss,
)
from deepmd.utils.graph import (
get_fitting_net_variables_from_graph_def,
)
from deepmd.utils.network import (
one_layer,
one_layer_rand_seed_shift,
)
[docs]@Fitting.register("dipole")
class DipoleFittingSeA(Fitting):
r"""Fit the atomic dipole with descriptor se_a.
Parameters
----------
descrpt : tf.Tensor
The descrptor
neuron : List[int]
Number of neurons in each hidden layer of the fitting net
resnet_dt : bool
Time-step `dt` in the resnet construction:
y = x + dt * \phi (Wx + b)
sel_type : List[int]
The atom types selected to have an atomic dipole prediction. If is None, all atoms are selected.
seed : int
Random seed for initializing the network parameters.
activation_function : str
The activation function in the embedding net. Supported options are |ACTIVATION_FN|
precision : str
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
"""
def __init__(
self,
descrpt: tf.Tensor,
neuron: List[int] = [120, 120, 120],
resnet_dt: bool = True,
sel_type: Optional[List[int]] = None,
seed: Optional[int] = None,
activation_function: str = "tanh",
precision: str = "default",
uniform_seed: bool = False,
**kwargs,
) -> None:
"""Constructor."""
self.ntypes = descrpt.get_ntypes()
self.dim_descrpt = descrpt.get_dim_out()
self.n_neuron = neuron
self.resnet_dt = resnet_dt
self.sel_type = sel_type
if self.sel_type is None:
self.sel_type = list(range(self.ntypes))
self.sel_mask = np.array(
[ii in self.sel_type for ii in range(self.ntypes)], dtype=bool
)
self.seed = seed
self.uniform_seed = uniform_seed
self.seed_shift = one_layer_rand_seed_shift()
self.fitting_activation_fn = get_activation_func(activation_function)
self.fitting_precision = get_precision(precision)
self.dim_rot_mat_1 = descrpt.get_dim_rot_mat_1()
self.dim_rot_mat = self.dim_rot_mat_1 * 3
self.useBN = False
self.fitting_net_variables = None
self.mixed_prec = None
[docs] def get_sel_type(self) -> int:
"""Get selected type."""
return self.sel_type
[docs] def get_out_size(self) -> int:
"""Get the output size. Should be 3."""
return 3
def _build_lower(self, start_index, natoms, inputs, rot_mat, suffix="", reuse=None):
# cut-out inputs
inputs_i = tf.slice(inputs, [0, start_index, 0], [-1, natoms, -1])
inputs_i = tf.reshape(inputs_i, [-1, self.dim_descrpt])
rot_mat_i = tf.slice(rot_mat, [0, start_index, 0], [-1, natoms, -1])
rot_mat_i = tf.reshape(rot_mat_i, [-1, self.dim_rot_mat_1, 3])
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) + 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,
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,
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
# (nframes x natoms) x naxis
final_layer = one_layer(
layer,
self.dim_rot_mat_1,
activation_fn=None,
name="final_layer" + suffix,
reuse=reuse,
seed=self.seed,
precision=self.fitting_precision,
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
# (nframes x natoms) x 1 * naxis
final_layer = tf.reshape(
final_layer, [tf.shape(inputs)[0] * natoms, 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, 3])
return final_layer
[docs] @cast_precision
def build(
self,
input_d: tf.Tensor,
rot_mat: tf.Tensor,
natoms: tf.Tensor,
input_dict: Optional[dict] = None,
reuse: Optional[bool] = None,
suffix: str = "",
) -> tf.Tensor:
"""Build the computational graph for fitting net.
Parameters
----------
input_d
The input descriptor
rot_mat
The rotation matrix from the descriptor.
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
input_dict
Additional dict for inputs.
reuse
The weights in the networks should be reused when get the variable.
suffix
Name suffix to identify this descriptor
Returns
-------
dipole
The atomic dipole.
"""
if input_dict is None:
input_dict = {}
type_embedding = input_dict.get("type_embedding", None)
atype = input_dict.get("atype", None)
nframes = input_dict.get("nframes")
start_index = 0
inputs = tf.reshape(input_d, [-1, natoms[0], self.dim_descrpt])
rot_mat = tf.reshape(rot_mat, [-1, natoms[0], self.dim_rot_mat])
if type_embedding is not None:
nloc_mask = tf.reshape(
tf.tile(tf.repeat(self.sel_mask, natoms[2:]), [nframes]), [nframes, -1]
)
atype_nall = tf.reshape(atype, [-1, natoms[1]])
# (nframes x nloc_masked)
self.atype_nloc_masked = tf.reshape(
tf.slice(atype_nall, [0, 0], [-1, natoms[0]])[nloc_mask], [-1]
) ## lammps will make error
self.nloc_masked = tf.shape(
tf.reshape(self.atype_nloc_masked, [nframes, -1])
)[1]
atype_embed = tf.nn.embedding_lookup(type_embedding, self.atype_nloc_masked)
else:
atype_embed = None
self.atype_embed = atype_embed
if atype_embed is None:
count = 0
outs_list = []
for type_i in range(self.ntypes):
if type_i not in self.sel_type:
start_index += natoms[2 + type_i]
continue
final_layer = self._build_lower(
start_index,
natoms[2 + type_i],
inputs,
rot_mat,
suffix="_type_" + str(type_i) + suffix,
reuse=reuse,
)
start_index += natoms[2 + type_i]
# concat the results
outs_list.append(final_layer)
count += 1
outs = tf.concat(outs_list, axis=1)
else:
inputs = tf.reshape(
tf.reshape(inputs, [nframes, natoms[0], self.dim_descrpt])[nloc_mask],
[-1, self.dim_descrpt],
)
rot_mat = tf.reshape(
tf.reshape(rot_mat, [nframes, natoms[0], self.dim_rot_mat_1 * 3])[
nloc_mask
],
[-1, self.dim_rot_mat_1, 3],
)
atype_embed = tf.cast(atype_embed, self.fitting_precision)
type_shape = atype_embed.get_shape().as_list()
inputs = tf.concat([inputs, atype_embed], axis=1)
self.dim_descrpt = self.dim_descrpt + type_shape[1]
inputs = tf.reshape(inputs, [nframes, self.nloc_masked, self.dim_descrpt])
rot_mat = tf.reshape(
rot_mat, [nframes, self.nloc_masked, self.dim_rot_mat_1 * 3]
)
final_layer = self._build_lower(
0, self.nloc_masked, inputs, rot_mat, suffix=suffix, reuse=reuse
)
# nframes x natoms x 3
outs = tf.reshape(final_layer, [nframes, self.nloc_masked, 3])
tf.summary.histogram("fitting_net_output", outs)
return tf.reshape(outs, [-1])
# return tf.reshape(outs, [tf.shape(inputs)[0] * natoms[0] * 3 // 3])
[docs] def init_variables(
self,
graph: tf.Graph,
graph_def: tf.GraphDef,
suffix: str = "",
) -> None:
"""Init the fitting 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
suffix to name scope
"""
self.fitting_net_variables = get_fitting_net_variables_from_graph_def(
graph_def, suffix=suffix
)
[docs] def enable_mixed_precision(self, mixed_prec: Optional[dict] = None) -> None:
"""Reveive the mixed precision setting.
Parameters
----------
mixed_prec
The mixed precision setting used in the embedding net
"""
self.mixed_prec = mixed_prec
self.fitting_precision = get_precision(mixed_prec["output_prec"])
[docs] def get_loss(self, loss: dict, lr) -> Loss:
"""Get the loss function.
Parameters
----------
loss : dict
the loss dict
lr : LearningRateExp
the learning rate
Returns
-------
Loss
the loss function
"""
return TensorLoss(
loss,
model=self,
tensor_name="dipole",
tensor_size=3,
label_name="dipole",
)