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Update Colvars library to version 2024-06-04

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This commit is contained in:
Giacomo Fiorin
2024-08-06 01:07:43 +02:00
parent 278accd9ea
commit 133dee9ac1
74 changed files with 7343 additions and 4676 deletions
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67
lib/colvars/colvarcomp_neuralnetwork.cpp
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@ -1,16 +1,31 @@
#if (__cplusplus >= 201103L)
// -*- c++ -*-
// This file is part of the Collective Variables module (Colvars).
// The original version of Colvars and its updates are located at:
// https://github.com/Colvars/colvars
// Please update all Colvars source files before making any changes.
// If you wish to distribute your changes, please submit them to the
// Colvars repository at GitHub.
#include "colvarmodule.h"
#include "colvarvalue.h"
#include "colvarparse.h"
#include "colvar.h"
#include "colvarcomp.h"
#include "colvar_neuralnetworkcompute.h"
using namespace neuralnetworkCV;
colvar::neuralNetwork::neuralNetwork(std::string const &conf): linearCombination(conf) {
colvar::neuralNetwork::neuralNetwork()
{
set_function_type("neuralNetwork");
}
int colvar::neuralNetwork::init(std::string const &conf)
{
int error_code = linearCombination::init(conf);
if (error_code != COLVARS_OK) return error_code;
// the output of neural network consists of multiple values
// read "output_component" key to determine it
get_keyval(conf, "output_component", m_output_index);
@ -59,8 +74,8 @@ colvar::neuralNetwork::neuralNetwork(std::string const &conf): linearCombination
std::string function_name;
get_keyval(conf, lookup_key.c_str(), function_name, std::string(""));
if (activation_function_map.find(function_name) == activation_function_map.end()) {
cvm::error("Unknown activation function name: \"" + function_name + "\".\n");
return;
return cvm::error("Unknown activation function name: \"" + function_name + "\".\n",
COLVARS_INPUT_ERROR);
}
activation_functions.push_back(std::make_pair(false, function_name));
cvm::log(std::string{"The activation function for layer["} + cvm::to_str(num_activation_functions + 1) + std::string{"] is "} + function_name + '\n');
@ -79,11 +94,13 @@ colvar::neuralNetwork::neuralNetwork(std::string const &conf): linearCombination
}
// expect the three numbers are equal
if ((num_layers_weight != num_layers_bias) || (num_layers_bias != num_activation_functions)) {
cvm::error("Error: the numbers of weights, biases and activation functions do not match.\n");
return;
return cvm::error(
"Error: the numbers of weights, biases and activation functions do not match.\n",
COLVARS_INPUT_ERROR);
}
// nn = std::make_unique<neuralnetworkCV::neuralNetworkCompute>();
// std::make_unique is only available in C++14
if (nn) nn.reset();
nn = std::unique_ptr<neuralnetworkCV::neuralNetworkCompute>(new neuralnetworkCV::neuralNetworkCompute());
for (size_t i_layer = 0; i_layer < num_layers_weight; ++i_layer) {
denseLayer d;
@ -93,8 +110,9 @@ colvar::neuralNetwork::neuralNetwork(std::string const &conf): linearCombination
try {
d = denseLayer(weight_files[i_layer], bias_files[i_layer], activation_functions[i_layer].second);
} catch (std::exception &ex) {
cvm::error("Error on initializing layer " + cvm::to_str(i_layer) + " (" + ex.what() + ")\n", COLVARS_INPUT_ERROR);
return;
return cvm::error("Error on initializing layer " + cvm::to_str(i_layer) +
" (" + ex.what() + ")\n",
COLVARS_INPUT_ERROR);
}
} else {
#endif
@ -104,8 +122,9 @@ colvar::neuralNetwork::neuralNetwork(std::string const &conf): linearCombination
try {
d = denseLayer(weight_files[i_layer], bias_files[i_layer], f, df);
} catch (std::exception &ex) {
cvm::error("Error on initializing layer " + cvm::to_str(i_layer) + " (" + ex.what() + ")\n", COLVARS_INPUT_ERROR);
return;
return cvm::error("Error on initializing layer " + cvm::to_str(i_layer) +
" (" + ex.what() + ")\n",
COLVARS_INPUT_ERROR);
}
#ifdef LEPTON
}
@ -123,11 +142,11 @@ colvar::neuralNetwork::neuralNetwork(std::string const &conf): linearCombination
}
}
} else {
cvm::error("Error: error on adding a new dense layer.\n");
return;
return cvm::error("Error: error on adding a new dense layer.\n", COLVARS_INPUT_ERROR);
}
}
nn->input().resize(cv.size());
return error_code;
}
colvar::neuralNetwork::~neuralNetwork() {
@ -185,4 +204,24 @@ void colvar::neuralNetwork::apply_force(colvarvalue const &force) {
}
}
#endif
cvm::real colvar::neuralNetwork::dist2(colvarvalue const &x1, colvarvalue const &x2) const
{
return x1.dist2(x2);
}
colvarvalue colvar::neuralNetwork::dist2_lgrad(colvarvalue const &x1, colvarvalue const &x2) const
{
return x1.dist2_grad(x2);
}
colvarvalue colvar::neuralNetwork::dist2_rgrad(colvarvalue const &x1, colvarvalue const &x2) const
{
return x2.dist2_grad(x1);
}
void colvar::neuralNetwork::wrap(colvarvalue & /* x_unwrapped */) const {}