Belle II Software  release-08-01-10
GRLNeuro.cc
1 /**************************************************************************
2  * basf2 (Belle II Analysis Software Framework) *
3  * Author: The Belle II Collaboration *
4  * *
5  * See git log for contributors and copyright holders. *
6  * This file is licensed under LGPL-3.0, see LICENSE.md. *
7  **************************************************************************/
8 
9 #include <framework/logging/Logger.h>
10 #include <trg/grl/GRLNeuro.h>
11 #include <trg/grl/dataobjects/GRLMLP.h>
12 
13 #include <cdc/geometry/CDCGeometryPar.h>
14 #include <framework/gearbox/Const.h>
15 #include <framework/gearbox/Unit.h>
16 #include <framework/datastore/StoreObjPtr.h>
17 #include <framework/datastore/StoreArray.h>
18 #include <trg/cdc/dbobjects/CDCTriggerNeuroConfig.h>
19 #include <trg/cdc/dataobjects/CDCTriggerTrack.h>
20 #include <string>
21 #include <cmath>
22 #include <TFile.h>
23 
24 using namespace Belle2;
25 using namespace CDC;
26 using namespace std;
27 
28 void
29 GRLNeuro::initialize(const Parameters& p)
30 {
31  // check parameters
32  bool okay = true;
33  // ensure that length of lists matches number of sectors
34  if (p.nHidden.size() != 1 && p.nHidden.size() != p.nMLP) {
35  B2ERROR("Number of nHidden lists should be 1 or " << p.nMLP);
36  okay = false;
37  }
38  if (p.outputScale.size() != 1 && p.outputScale.size() != p.nMLP) {
39  B2ERROR("Number of outputScale lists should be 1 or " << p.nMLP);
40  okay = false;
41  }
42  // ensure that number of target nodes is valid
43  unsigned short nTarget = int(p.targetresult);
44  if (nTarget < 1) {
45  B2ERROR("No outputs! Turn on targetresult.");
46  okay = false;
47  }
48  for (unsigned iScale = 0; iScale < p.outputScale.size(); ++iScale) {
49  if (p.outputScale[iScale].size() != 2 * nTarget) {
50  B2ERROR("outputScale should be exactly " << 2 * nTarget << " values");
51  okay = false;
52  }
53  }
54 
55  if (!okay) return;
56  // initialize MLPs
57  m_MLPs.clear();
58  for (unsigned iMLP = 0; iMLP < p.nMLP; ++iMLP) {
59  //get indices for sector parameters
60  unsigned short nInput = p.i_cdc_sector[iMLP] + p.i_ecl_sector[iMLP];
61  vector<float> nhidden = p.nHidden[iMLP];
62  vector<unsigned short> nNodes = {nInput};
63  for (unsigned iHid = 0; iHid < nhidden.size(); ++iHid) {
64  if (p.multiplyHidden) {
65  nNodes.push_back(nhidden[iHid] * nNodes[0]);
66  } else {
67  nNodes.push_back(nhidden[iHid]);
68  }
69  }
70  nNodes.push_back(nTarget);
71  unsigned short targetVars = int(p.targetresult);
72  vector<float> outputScale = (p.outputScale.size() == 1) ? p.outputScale[0] : p.outputScale[iMLP];
73  m_MLPs.push_back(GRLMLP(nNodes, targetVars, outputScale));
74  }
75 }
76 
77 
78 float
79 GRLNeuro::relu(float x)
80 {
81  if (x < 0) return 0;
82  else return x;
83 
84 }
85 
86 float
87 GRLNeuro::runMLP(unsigned isector, const std::vector<float>& input)
88 {
89  const GRLMLP& expert = m_MLPs[isector];
90  vector<float> weights = expert.getWeights();
91  vector<float> bias = expert.getBias();
92  //vector<float> layerinput = {38.0,33.0,0.0,0.0,0.0,0.0,59.0,29.0,0.0,0.0,0.0,0.0,98.0,204.0,0.0,0.0,0.0,0.0,2.0};
93  vector<float> layerinput = input;
94  //for (int iin = 0 ; iin < input.size(); iin++) std::cout<< input[iin] << ",";
95  vector<float> layeroutput2 = {};
96  vector<float> layeroutput3 = {};
97  vector<float> layeroutput4 = {};
98  unsigned iw = 0;
99  unsigned ib = 0;
100  //loop over 1 -> 2 layer
101  layeroutput2.clear();
102  layeroutput2.assign(expert.getNumberOfNodesLayer(1), 0.);
103  for (unsigned io = 0; io < layeroutput2.size(); ++io) {
104  //loop over inputs
105  for (unsigned ii = 0; ii < layerinput.size(); ++ii) {
106  //std::cout<< " layerinput[" << ii <<"]:" << layerinput[ii]<< " weight: "<< weights[iw] << "muti: " <<layerinput[ii] * weights[iw] << endl;
107  layeroutput2[io] += layerinput[ii] * weights[iw++];
108  }
109  //std::cout<< " bias: "<< bias[ib] << endl;
110  layeroutput2[io] += bias[ib++];
111  //apply activation function, ReLU and convert to float_to_fixed(x, 11, 0)
112  layeroutput2[io] = relu(floor(layeroutput2[io]));
113  //std::cout<< " layeroutput2["<< io <<"]:" << layeroutput2[io] << endl;
114  }
115  //apply activation function, ReLU for hidden layer, and Sigmoid for output layer
116 
117  //loop over 2 -> 3 layer
118  layeroutput3.clear();
119  layeroutput3.assign(expert.getNumberOfNodesLayer(2), 0.);
120  for (unsigned io = 0; io < layeroutput3.size(); ++io) {
121  for (unsigned ii = 0; ii < layeroutput2.size(); ++ii) {
122  //std::cout<< " layerinput["<< ii <<"]:" << layerinput[ii]<< " weight: "<< weights[iw] << "muti: " <<layerinput[ii] * weights[iw] << endl;
123  layeroutput3[io] += layeroutput2[ii] * weights[iw++];
124  }
125  // std::cout<< " layeroutput["<< io <<"]:" << layeroutput[io] << " bias: "<< bias[ib] << endl;
126  layeroutput3[io] += bias[ib++];
127  //apply activation function, ReLU and convert to float_to_fixed(x, 7, 3)
128  layeroutput3[io] = relu(float_to_fixed(layeroutput3[io], 7, 3)) ;
129  //std::cout<< " layeroutput["<< io <<"]:" << layeroutput[io] << " il: "<< il << endl;
130  }
131 
132  //loop over 3 -> 4 layer
133  layeroutput4.clear();
134  layeroutput4.assign(expert.getNumberOfNodesLayer(3), 0.);
135  for (unsigned io = 0; io < layeroutput4.size(); ++io) {
136  for (unsigned ii = 0; ii < layeroutput3.size(); ++ii) {
137  //std::cout<< " layerinput["<< ii <<"]:" << layerinput[ii]<< " weight: "<< weights[iw] << "muti: " <<layerinput[ii] * weights[iw] << endl;
138  layeroutput4[io] += layeroutput3[ii] * weights[iw++];
139  }
140  // std::cout<< " layeroutput["<< io <<"]:" << layeroutput[io] << " bias: "<< bias[ib] << endl;
141  layeroutput4[io] += bias[ib++];
142  //convert to float_to_fixed(x, 6, 2)
143  layeroutput4[io] = float_to_fixed(layeroutput4[io], 6, 2) ;
144  //apply activation function, sigmiod and convert to float_to_fixed(x, 1, 6)
145  layeroutput4[io] = mysigmiod(layeroutput4[io]);
146  layeroutput4[io] = float_to_fixed(layeroutput4[io], 1, 6);
147  //std::cout<< " layeroutput["<< io <<"]:" << layeroutput[io] << " il: "<< il << endl;
148  }
149 
150  //std::cout<< "final value"<< layeroutput4[0] << std::endl;
151  return layeroutput4[0];
152 }
153 
154 bool GRLNeuro::load(unsigned isector, const string& weightfilename, const string& biasfilename)
155 {
156  if (weightfilename.size() < 1) {
157  B2ERROR("Could not load Neurotrigger weights from database!");
158  return false;
159  } else if (biasfilename.size() < 1) {
160  B2ERROR("Could not load Neurotrigger bias from database!");
161  return false;
162  } else {
163  std::ifstream wfile(weightfilename);
164  if (!wfile.is_open()) {
165  B2WARNING("Could not open file " << weightfilename);
166  return false;
167  } else {
168  std::ifstream bfile(biasfilename);
169  if (!bfile.is_open()) {
170  B2WARNING("Could not open file " << biasfilename);
171  return false;
172  } else {
173  GRLMLP& expert = m_MLPs[isector];
174  std::vector<float> warray;
175  std::vector<float> barray;
176  warray.clear();
177  barray.clear();
178 
179  float element;
180  while (wfile >> element) {
181  warray.push_back(element);
182  }
183  while (bfile >> element) {
184  barray.push_back(element);
185  }
186 
187  if (warray.size() != expert.nWeightsCal()) {
188  B2ERROR("Number of weights is not equal to registered architecture!");
189  return false;
190  } else expert.setWeights(warray);
191  if (barray.size() != expert.nBiasCal()) {
192  B2ERROR("Number of bias is not equal to registered architecture!");
193  return false;
194  }
195  //change the precision based in FPGA (hls4ml)
196  for (int it = 0; it < warray.size(); it++) {
197  if (it < 380) warray[it] = float_to_fixed(warray[it], 2, 13);
198  else if (it < 780) warray[it] = float_to_fixed(warray[it], 1, 13);
199  else warray[it] = float_to_fixed(warray[it], 2, 11);
200  }
201 
202  for (int it = 0; it < barray.size(); it++) {
203  if (it < 20) barray[it] = float_to_fixed(barray[it], 4, 2);
204  else if (it < 40) barray[it] = float_to_fixed(barray[it], 4, 3);
205  else barray[it] = float_to_fixed(barray[it], 0, 2);
206  }
207 
208  expert.setWeights(warray);
209  expert.setBias(barray);
210  return true;
211  }
212  }
213  }
214 }
215 
216 float GRLNeuro::float_to_fixed(float num, int m, int n)
217 {
218  // integer: 1 bit for sign and others are values
219  //Get integer and decimal digits
220  int integer_part = num;
221  float fractional_part = num - integer_part;
222 
223  //floor to the nearest decimal digit
224  fractional_part = floor(fractional_part * (1 << n)) * (1.0 / (1 << n)) ;
225 
226  //Remove the overflow of integer bits
227  int final_integer = 0;
228  if (m > 0) {
229  if (std::abs(integer_part) < ((1 << (m - 1)) - 1)) {
230  final_integer = std::abs(integer_part);
231  final_integer = (final_integer & ((1 << (m - 1)) - 1)) ;
232  final_integer = (integer_part & (1 << (m - 1))) > 0 ? (final_integer) * (-1) : final_integer;
233  } else {
234  final_integer = integer_part;
235  final_integer = (final_integer & ((1 << (m - 1)) - 1)) ;
236  final_integer = (integer_part & (1 << (m - 1))) > 0 ? (final_integer) * (-1) : final_integer;
237  }
238  }
239 
240  float final_value = final_integer + fractional_part;
241  return final_value;
242 
243 }
244 
245 float GRLNeuro::mysigmiod(float num)
246 {
247  const float sigmoid_table[1024] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.000976563, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00195313, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00292969, 0.00390625, 0.00390625, 0.00390625, 0.00390625, 0.00390625, 0.00390625, 0.00390625, 0.00390625, 0.00390625, 0.00390625, 0.00390625, 0.00390625, 0.00390625, 0.00390625, 0.00488281, 0.00488281, 0.00488281, 0.00488281, 0.00488281, 0.00488281, 0.00488281, 0.00488281, 0.00488281, 0.00488281, 0.00488281, 0.00488281, 0.00585938, 0.00585938, 0.00585938, 0.00585938, 0.00585938, 0.00585938, 0.00585938, 0.00585938, 0.00585938, 0.00585938, 0.00683594, 0.00683594, 0.00683594, 0.00683594, 0.00683594, 0.00683594, 0.00683594, 0.00683594, 0.0078125, 0.0078125, 0.0078125, 0.0078125, 0.0078125, 0.0078125, 0.0078125, 0.0078125, 0.00878906, 0.00878906, 0.00878906, 0.00878906, 0.00878906, 0.00878906, 0.00878906, 0.00976563, 0.00976563, 0.00976563, 0.00976563, 0.00976563, 0.00976563, 0.0107422, 0.0107422, 0.0107422, 0.0107422, 0.0107422, 0.0107422, 0.0117188, 0.0117188, 0.0117188, 0.0117188, 0.0117188, 0.0126953, 0.0126953, 0.0126953, 0.0126953, 0.0126953, 0.0136719, 0.0136719, 0.0136719, 0.0136719, 0.0146484, 0.0146484, 0.0146484, 0.0146484, 0.015625, 0.015625, 0.015625, 0.015625, 0.0166016, 0.0166016, 0.0166016, 0.0166016, 0.0175781, 0.0175781, 0.0175781, 0.0175781, 0.0185547, 0.0185547, 0.0185547, 0.0195313, 0.0195313, 0.0195313, 0.0205078, 0.0205078, 0.0205078, 0.0214844, 0.0214844, 0.0214844, 0.0224609, 0.0224609, 0.0224609, 0.0234375, 0.0234375, 0.0244141, 0.0244141, 0.0244141, 0.0253906, 0.0253906, 0.0253906, 0.0263672, 0.0263672, 0.0273438, 0.0273438, 0.0283203, 0.0283203, 0.0292969, 0.0292969, 0.0292969, 0.0302734, 0.0302734, 0.03125, 0.03125, 0.0322266, 0.0322266, 0.0332031, 0.0332031, 0.0341797, 0.0341797, 0.0351563, 0.0361328, 0.0361328, 0.0371094, 0.0371094, 0.0380859, 0.0380859, 0.0390625, 0.0400391, 0.0400391, 0.0410156, 0.0419922, 0.0419922, 0.0429688, 0.0439453, 0.0439453, 0.0449219, 0.0458984, 0.0458984, 0.046875, 0.0478516, 0.0488281, 0.0488281, 0.0498047, 0.0507813, 0.0517578, 0.0517578, 0.0527344, 0.0537109, 0.0546875, 0.0556641, 0.0566406, 0.0566406, 0.0576172, 0.0585938, 0.0595703, 0.0605469, 0.0615234, 0.0625, 0.0634766, 0.0644531, 0.0654297, 0.0664063, 0.0673828, 0.0683594, 0.0693359, 0.0703125, 0.0712891, 0.0722656, 0.0732422, 0.0742188, 0.0751953, 0.0761719, 0.0771484, 0.0791016, 0.0800781, 0.0810547, 0.0820313, 0.0830078, 0.0849609, 0.0859375, 0.0869141, 0.0878906, 0.0898438, 0.0908203, 0.0917969, 0.09375, 0.0947266, 0.0966797, 0.0976563, 0.0986328, 0.100586, 0.101563, 0.103516, 0.104492, 0.106445, 0.107422, 0.109375, 0.110352, 0.112305, 0.114258, 0.115234, 0.117188, 0.119141, 0.120117, 0.12207, 0.124023, 0.125, 0.126953, 0.128906, 0.130859, 0.132813, 0.134766, 0.135742, 0.137695, 0.139648, 0.141602, 0.143555, 0.145508, 0.147461, 0.149414, 0.151367, 0.15332, 0.155273, 0.157227, 0.160156, 0.162109, 0.164063, 0.166016, 0.167969, 0.170898, 0.172852, 0.174805, 0.177734, 0.179688, 0.181641, 0.18457, 0.186523, 0.189453, 0.191406, 0.194336, 0.196289, 0.199219, 0.201172, 0.204102, 0.206055, 0.208984, 0.211914, 0.213867, 0.216797, 0.219727, 0.222656, 0.224609, 0.227539, 0.230469, 0.233398, 0.236328, 0.239258, 0.242188, 0.244141, 0.24707, 0.25, 0.25293, 0.255859, 0.259766, 0.262695, 0.265625, 0.268555, 0.271484, 0.274414, 0.277344, 0.28125, 0.28418, 0.287109, 0.290039, 0.293945, 0.296875, 0.299805, 0.303711, 0.306641, 0.310547, 0.313477, 0.317383, 0.320313, 0.324219, 0.327148, 0.331055, 0.333984, 0.337891, 0.34082, 0.344727, 0.348633, 0.351563, 0.355469, 0.358398, 0.362305, 0.366211, 0.370117, 0.373047, 0.376953, 0.380859, 0.384766, 0.387695, 0.391602, 0.395508, 0.399414, 0.40332, 0.407227, 0.410156, 0.414063, 0.417969, 0.421875, 0.425781, 0.429688, 0.433594, 0.4375, 0.441406, 0.445313, 0.449219, 0.453125, 0.457031, 0.460938, 0.464844, 0.46875, 0.472656, 0.476563, 0.480469, 0.484375, 0.488281, 0.492188, 0.496094, 0.5, 0.50293, 0.506836, 0.510742, 0.514648, 0.518555, 0.522461, 0.526367, 0.530273, 0.53418, 0.538086, 0.541992, 0.545898, 0.549805, 0.553711, 0.557617, 0.561523, 0.56543, 0.569336, 0.573242, 0.577148, 0.581055, 0.584961, 0.588867, 0.591797, 0.595703, 0.599609, 0.603516, 0.607422, 0.611328, 0.614258, 0.618164, 0.62207, 0.625977, 0.628906, 0.632813, 0.636719, 0.640625, 0.643555, 0.647461, 0.650391, 0.654297, 0.658203, 0.661133, 0.665039, 0.667969, 0.671875, 0.674805, 0.678711, 0.681641, 0.685547, 0.688477, 0.692383, 0.695313, 0.699219, 0.702148, 0.705078, 0.708984, 0.711914, 0.714844, 0.717773, 0.72168, 0.724609, 0.727539, 0.730469, 0.733398, 0.736328, 0.739258, 0.743164, 0.746094, 0.749023, 0.751953, 0.754883, 0.756836, 0.759766, 0.762695, 0.765625, 0.768555, 0.771484, 0.774414, 0.776367, 0.779297, 0.782227, 0.785156, 0.787109, 0.790039, 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248 
249  int data_round = num * 1024 / 16;
250  int index = data_round + 8 * 1024 / 16;
251  if (index < 0) index = 0;
252  if (index > 1023) index = 1023;
253  return sigmoid_table[index];
254 
255 }
Belle2::GRLMLP
Class to keep all parameters of an expert MLP for the neuro trigger.
Definition: GRLMLP.h:21
Belle2::GRLNeuro::initialize
void initialize(const Parameters &p)
Set parameters and get some network independent parameters.
Definition: GRLNeuro.cc:29
Belle2::GRLNeuro::load
bool load(unsigned isector, const std::string &wfilename, const std::string &bfilename)
Load MLPs from file.
Definition: GRLNeuro.cc:154
Belle2::GRLNeuro::mysigmiod
float mysigmiod(float num)
discrete sigmoid activation function (1024 bins)
Definition: GRLNeuro.cc:245
Belle2::GRLNeuro::runMLP
float runMLP(unsigned isector, const std::vector< float > &input)
Run an expert MLP.
Definition: GRLNeuro.cc:87
Belle2::GRLNeuro::relu
float relu(float x)
ReLu activation function.
Definition: GRLNeuro.cc:79
Belle2::GRLNeuro::float_to_fixed
float float_to_fixed(float num, int m, int n)
change the percision of number, m = number of integer bits, n = number of decimal
Definition: GRLNeuro.cc:216
Belle2
Abstract base class for different kinds of events.
Definition: MillepedeAlgorithm.h:17
Belle2::GRLNeuro::Parameters
Struct to keep neurotrigger parameters.
Definition: GRLNeuro.h:41