先上效果图如下:
import numpy as np
import matplotlib.pyplot as plt
import random
def Statistics_weight(save_dir, type, name, weight):
if not os.path.exists(save_dir):
os.mkdir(save_dir)
weight_abs = abs(weight)
max_val = np.max(weight_abs)
min_val = np.min(weight_abs)
################################################
x_data = [0, 1e-25, 1e-15, 1e-10, 1e-5, 1e-1, 1, 2, 10]
x_data_show = ["0", "1e-25", "1e-15", "1e-10", "1e-5", "1e-1", "1", "2", "10"]
y_data = []
for i in range(len(x_data)):
if 0 == i:
tmp0 = weight_abs >= 0
else:
tmp0 = weight_abs >= x_data[i-1]
if 0 == x_data[i]:
tmp1 = weight_abs <= x_data[i]
else:
tmp1 = weight_abs < x_data[i]
pos_right = (np.multiply(tmp0, tmp1)).sum()
ratio = pos_right * 1.0 / weight_abs.size
y_data.append(ratio)
################################################
# print(x_data)
# print(y_data)
plt.figure(name)
# 画图,plt.bar()可以画柱状图
for i in range(len(x_data)):
plt.bar(x_data_show[i], y_data[i])
for a, b in zip(x_data_show, y_data):
plt.text(a, b + 0.005, ("%.2f" % b), ha='center', va='bottom', fontsize=11)
# 设置图片名称
plt.title(type + "_" + name)
# 设置x轴标签名
plt.xlabel("value")
# 设置y轴标签名
plt.ylabel("ratio")
plt.savefig(os.path.join(save_dir, type + "_" + name+".png"))
# 显示
# plt.show()
####conv
print("==========>>conv" * 5)
total_weight = 0
total_weight_avail = 0
for layer_para_name, para in net.params.items():
if "bn" in layer_para_name or "scale" in layer_para_name or "Scale" in layer_para_name or "bias" in layer_para_name:
continue
Statistics_weight("/media/xxx_sparse/caffe-jacinto/0000/deply/show/0930/0930_L1+sprse", "L1+sparse", layer_para_name, abs(para[0].data))
weights_np = abs(para[0].data) # para[0]weight para[1]bias 2 128 3 3
weights_np_0 = weights_np[0]
tmp_2 = weights_np <= 0.2
ratio_123 = tmp_2.sum() * 1.0 / weights_np.size
total_weight += weights_np.size
tmp = weights_np > T
total_weight_avail += tmp.sum()
ratio_zero = (1 - (tmp.sum() * 1.0 / weights_np.size))
print("layer_para_name=", layer_para_name, " ratio_zero=", ratio_zero)
print("ratio_conv_avail_weight=", total_weight_avail * 1.0 / total_weight, " ratio_conv_not_avail_weight=",
1 - total_weight_avail * 1.0 / total_weight)
##################################
c++ 加在blob.hpp里面的代码:
double statistics_weight(const string name, int start, int n, const float &max_threshold_value, const float &threshold_fraction_selected)
{
const double* data_vec = cpu_data<double>() + start;
double max_tmp = -DBL_MIN;
// double min_tmp = -DBL_MAX;
// cv::rectangle();
for(int i=0; i< n; i++)
{
max_tmp = abs(data_vec[i]) > max_tmp ? abs(data_vec[i]) : max_tmp;
}
int split = 10;
float each_ = max_tmp / split;
std::vector<int> Histogram_(split, 0);
for(int i=0; i< n; i++)
{
int idx = abs(data_vec[i]) / each_;
if(split == idx)
{
idx -= 1;
}
Histogram_[idx] += 1;
}
int height_img = 500;
cv::Mat hist(height_img, height_img*1.8, CV_8UC3, cv::Scalar(0,0,0));
int T_hist_width = 60;
int T_hist_gap = T_hist_width + 20;
for(int i=0;i<split;i++)
{
float ratio = Histogram_[i] * 1.0 / n;
int height = ratio * height_img;
cv::Point pt_tl = cv::Point(i*T_hist_gap, height_img - height);
cv::Point pt_br = cv::Point(i*T_hist_gap+T_hist_width,height_img - 0);
cv::rectangle(hist, pt_tl, pt_br, cv::Scalar(255,0,0), -1);
cv::putText(hist, std::to_string(ratio*100.0) + "%", cv::Point(pt_tl.x, pt_tl.y - 30), cv::FONT_HERSHEY_PLAIN, 1, cv::Scalar(0,255,255),1);
string str_each_1 = std::to_string((each_ * (i+1)));
int pos_decimal_point = str_each_1.find(".");
string str_each_new = str_each_1.substr(0,pos_decimal_point+3);
cv::putText(hist, str_each_new, cv::Point((pt_tl.x+pt_br.x)/2-5, height_img), cv::FONT_HERSHEY_PLAIN, 1, cv::Scalar(0,0,255),1);
cv::putText(hist, "max_threshold_value="+ std::to_string(max_threshold_value), cv::Point(hist.cols*0.25, 50), cv::FONT_HERSHEY_PLAIN, 1, cv::Scalar(0,255,0),1);
cv::putText(hist, "threshold_fraction_selected="+ std::to_string(threshold_fraction_selected*100) + "%", cv::Point(hist.cols*0.25, 120), cv::FONT_HERSHEY_PLAIN, 1, cv::Scalar(0,255,0),1);
}
cv::imshow("hist_"+name,hist);
cv::waitKey(0);
return max_tmp;
}
c++的在net.cpp的稀疏代码中调用:
for(int c=0; c<no; c++) {
// LOG(INFO) <<"=========>c="<<c;
int weight_count_channel = ni * kernel_shape_data[0] * kernel_shape_data[1] / num_group;
int start_index = weight_count_channel * c;
float max_abs = std::abs(conv_weights.max(start_index, weight_count_channel));
float min_abs = std::abs(conv_weights.min(start_index, weight_count_channel));
float max_abs_value = std::max<float>(max_abs, min_abs);
float step_size = max_abs_value * threshold_step_factor;
float max_threshold_value = std::min<float>(std::min<float>(threshold_value_max, max_abs_value*threshold_value_maxratio), max_abs_value);
float aa = conv_weights.statistics_weight(layer_name, start_index, weight_count_channel, max_threshold_value, threshold_fraction_selected);
bool verbose_th_val = false;
if(verbose && verbose_th_val || 0) {////////
if ((max_abs_value*threshold_value_maxratio) > threshold_value_max) {
LOG(INFO) << "threshold_value_max " << threshold_value_max;
LOG(INFO) << "threshold_value_maxratio " << threshold_value_maxratio;
LOG(INFO) << "max_abs_value*threshold_value_maxratio " << (max_abs_value*threshold_value_maxratio);
LOG(INFO) << "final threshold_value used" << max_threshold_value;
}
}
标签:统计分析,权重,weight,max,abs,caffe,para,data,name
From: https://www.cnblogs.com/yanghailin/p/16745793.html