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denghuiludenghuilu
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Merge pull request #9 from deepmodeling/devel
devel update
2 parents f0453ff + 6cceef0 commit 5a88334

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‎examples/water/train/polar.json‎

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"learning_rate" :{
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"type": "exp",
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"start_lr": 0.001,
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"decay_steps": 5000,
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"decay_rate": 0.95,
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"start_lr": 0.001,
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"stop_lr": 3.51e-8,
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"_comment": "that's all"
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},
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‎examples/water/train/polar_se_a.json‎

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"_comment": " model parameters",
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"model":{
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"type_map": ["O", "H"],
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"data_stat_nbatch": 1,
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"data_stat_nbatch": 10,
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"descriptor" :{
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"type": "se_a",
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"sel": [46, 92],
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"fitting_net": {
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"type": "polar",
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"sel_type": [0],
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"fit_diag": true,
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"fit_diag": false,
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"neuron": [100, 100, 100],
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"resnet_dt": true,
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"seed": 1,
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"learning_rate" :{
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"type": "exp",
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"start_lr": 0.01,
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"decay_steps": 5000,
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"decay_rate": 0.95,
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"start_lr": 0.01,
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"stop_lr": 3.51e-7,
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"_comment": "that's all"
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},
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‎examples/water/train/wannier.json‎

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"learning_rate" :{
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"type": "exp",
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"start_lr": 0.001,
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"decay_steps": 5000,
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"decay_rate": 0.95,
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"start_lr": 0.001,
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"stop_lr": 3.51e-8,
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"_comment": "that's all"
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},
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‎examples/water/train/water.json‎

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"_comment": " model parameters",
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"model":{
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"type_map": ["O", "H"],
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"data_stat_nbatch": 10,
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"descriptor": {
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"type": "loc_frame",
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"sel_a": [16, 32],
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"learning_rate" :{
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"type": "exp",
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"start_lr": 0.001,
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"decay_steps": 5000,
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"decay_rate": 0.95,
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"start_lr": 0.001,
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"stop_lr": 3.51e-8,
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"_comment": "that's all"
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},
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‎examples/water/train/water_se_a.json‎

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"learning_rate" :{
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"type": "exp",
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"start_lr": 0.001,
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"decay_steps": 5000,
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"decay_rate": 0.95,
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"start_lr": 0.001,
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"stop_lr": 3.51e-8,
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"_comment": "that's all"
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},
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‎examples/water/train/water_se_ar.json‎

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"learning_rate" :{
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"type": "exp",
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"start_lr": 0.005,
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"decay_steps": 5000,
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"decay_rate": 0.95,
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"start_lr": 0.005,
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"stop_lr": 1.76e-7,
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"_comment": "that's all"
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},
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‎examples/water/train/water_se_r.json‎

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},
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"learning_rate" : {
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"start_lr": 0.005,
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"type": "exp",
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"decay_steps": 5000,
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"decay_rate": 0.95,
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"start_lr": 0.005,
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"stop_lr": 1.76e-7,
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"_comment": " that's all"
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},
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‎examples/water/train/water_srtab_example.json‎

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"learning_rate" :{
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"type": "exp",
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"start_lr": 0.001,
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"decay_steps": 5000,
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"decay_rate": 0.95,
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"start_lr": 0.001,
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"stop_lr": 3.51e-8,
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"_comment": "that's all"
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},
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‎source/lib/include/DataModifier.h‎

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#pragma once
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#include "NNPInter.h"
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class DataModifier
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{
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public:
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DataModifier();
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DataModifier(const string & model,
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const int & gpu_rank = 0,
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const string & name_scope = "");
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~DataModifier () {};
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void init (const string & model,
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const int & gpu_rank = 0,
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const string & name_scope = "");
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void print_summary(const string &pre) const;
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public:
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void compute (vector<VALUETYPE> & dfcorr_,
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vector<VALUETYPE> & dvcorr_,
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const vector<VALUETYPE> & dcoord_,
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const vector<int> & datype_,
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const vector<VALUETYPE> & dbox,
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const vector<pair<int,int>> & pairs,
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const vector<VALUETYPE> & delef_,
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const int nghost,
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const LammpsNeighborList & lmp_list);
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VALUETYPE cutoff () const {assert(inited); return rcut;};
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int numb_types () const {assert(inited); return ntypes;};
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vector<int> sel_types () const {assert(inited); return sel_type;};
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private:
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Session* session;
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string name_scope, name_prefix;
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int num_intra_nthreads, num_inter_nthreads;
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GraphDef graph_def;
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bool inited;
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VALUETYPE rcut;
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VALUETYPE cell_size;
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int ntypes;
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string model_type;
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vector<int> sel_type;
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template<class VT> VT get_scalar(const string & name) const;
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template<class VT> void get_vector(vector<VT> & vec, const string & name) const;
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void run_model (vector<VALUETYPE> & dforce,
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vector<VALUETYPE> & dvirial,
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Session * session,
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const std::vector<std::pair<string, Tensor>> & input_tensors,
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const NNPAtomMap<VALUETYPE> & nnpmap,
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const int nghost);
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};
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‎source/lib/include/DeepTensor.h‎

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#pragma once
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#include "NNPInter.h"
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class DeepTensor
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{
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public:
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DeepTensor();
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DeepTensor(const string & model,
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const int & gpu_rank = 0,
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const string &name_scope = "");
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void init (const string & model,
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const int & gpu_rank = 0,
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const string &name_scope = "");
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void print_summary(const string &pre) const;
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public:
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void compute (vector<VALUETYPE> & value,
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const vector<VALUETYPE> & coord,
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const vector<int> & atype,
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const vector<VALUETYPE> & box,
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const int nghost = 0);
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void compute (vector<VALUETYPE> & value,
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const vector<VALUETYPE> & coord,
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const vector<int> & atype,
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const vector<VALUETYPE> & box,
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const int nghost,
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const LammpsNeighborList & lmp_list);
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VALUETYPE cutoff () const {assert(inited); return rcut;};
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int numb_types () const {assert(inited); return ntypes;};
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int output_dim () const {assert(inited); return odim;};
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const vector<int> & sel_types () const {assert(inited); return sel_type;};
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private:
33+
Session* session;
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string name_scope;
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int num_intra_nthreads, num_inter_nthreads;
36+
GraphDef graph_def;
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bool inited;
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VALUETYPE rcut;
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VALUETYPE cell_size;
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int ntypes;
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string model_type;
42+
int odim;
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vector<int> sel_type;
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template<class VT> VT get_scalar(const string & name) const;
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template<class VT> void get_vector (vector<VT> & vec, const string & name) const;
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void run_model (vector<VALUETYPE> & d_tensor_,
47+
Session * session,
48+
const std::vector<std::pair<string, Tensor>> & input_tensors,
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const NNPAtomMap<VALUETYPE> & nnpmap,
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const int nghost = 0);
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void compute_inner (vector<VALUETYPE> & value,
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const vector<VALUETYPE> & coord,
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const vector<int> & atype,
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const vector<VALUETYPE> & box,
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const int nghost = 0);
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void compute_inner (vector<VALUETYPE> & value,
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const vector<VALUETYPE> & coord,
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const vector<int> & atype,
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const vector<VALUETYPE> & box,
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const int nghost,
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const InternalNeighborList&lmp_list);
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};
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