1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
|
#include "caffe2/core/logging.h"
#include "l2_minimization.h"
#include <cassert>
#include <cmath>
namespace dnnlowp {
TensorQuantizationParams P99::ChooseQuantizationParams(
const Histogram& hist,
bool preserve_sparsity,
int precision) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
float min, max;
std::vector<float> bins_f(
dnnlowp::adjust_hist_to_include_zero(hist, &min, &max));
int nbins = bins_f.size();
CAFFE_ENFORCE(min <= 0.f);
CAFFE_ENFORCE(max >= 0.f);
float org_max = max;
float org_min = min;
// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
float bin_width = (max - min) / nbins;
double total_sum = 0;
for (int i = 0; i < nbins; ++i) {
total_sum += bins_f[i];
}
double sum = 0;
std::vector<double> CDF(nbins, 0.f);
for (int i = 0; i < nbins; ++i) {
sum += bins_f[i];
CDF[i] = (double)sum / total_sum;
}
CAFFE_ENFORCE(threshold_ > 0.5 && threshold_ < 1);
double left_quantile = (1.0f - threshold_) / 2.0f;
double right_quantile = 1.0f - left_quantile;
int i_begin = 0;
int i_end = nbins - 2;
bool finished = false;
while (i_begin <= i_end && !finished) {
finished = true;
if (CDF[i_begin] < left_quantile) {
i_begin++;
finished = false;
}
if (CDF[i_end] > right_quantile) {
finished = false;
i_end--;
}
}
// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
min = i_begin * bin_width + org_min;
// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
max = (i_end + 2) * bin_width + org_min;
VLOG(2) << "Org min " << org_min << " org max " << org_max << " found min "
<< min << " max " << max;
QuantizationFactory* qfactory = QuantizationFactory::GetDefaultInstance();
return qfactory->ChooseQuantizationParams(
min, max, precision, preserve_sparsity);
} // ChooseQuantizationParams
} // namespace dnnlowp
|
