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#include "caffe2/core/logging.h"
#include "l2_minimization.h"
#include <fstream>
#include <iostream>
#include <sstream>
using namespace std;
using namespace dnnlowp;
int main(int argc, const char* argv[]) {
if (argc < 3) {
cerr << "Usage: " << argv[0]
<< " in_file out_file [preserve_sparsity] [precision]" << endl;
return -1;
}
ifstream in(argv[1]);
ofstream out(argv[2]);
bool preserve_sparsity = argc >= 4 ? atoi(argv[3]) : false;
int precision = argc >= 5 ? atoi(argv[4]) : 8;
vector<tuple<int, string, int, string>> infos;
vector<Histogram> hists;
string line;
while (getline(in, line)) {
istringstream ist(line);
int op_index, output_index;
string op_type, tensor_name;
float min, max;
int nbins;
ist >> op_index >> op_type >> output_index >> tensor_name >> min >> max >>
nbins;
infos.push_back(tuple<int, string, int, string>(
op_index, op_type, output_index, tensor_name));
vector<uint64_t> bins;
for (int i = 0; i < nbins; ++i) {
uint64_t cnt;
ist >> cnt;
bins.push_back(cnt);
}
assert(bins.size() == nbins);
Histogram hist = Histogram(min, max, bins);
hists.emplace_back(min, max, bins);
}
vector<TensorQuantizationParams> qparams(hists.size());
for (int i = 0; i < hists.size(); ++i) {
qparams[i] = L2ErrorMinimization().NonlinearQuantizationParamsSearch(
hists[i], preserve_sparsity, precision);
}
for (int i = 0; i < qparams.size(); ++i) {
VLOG(2) << std::get<2>(infos[i]);
out << std::get<0>(infos[i]) << " " << std::get<1>(infos[i]) << " "
<< std::get<2>(infos[i]) << " " << std::get<3>(infos[i]) << " "
<< qparams[i].Min() << " " << qparams[i].Max() << endl;
}
return 0;
}
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