From 168af40fe9a3cc81c6ee16b3e81f154780c36bdb Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 03 六月 2021 15:03:27 +0800
Subject: [PATCH] up new v4
---
lib/detecter_tools/darknet/matrix.c | 664 +++++++++++++++++++++++++++---------------------------
1 files changed, 332 insertions(+), 332 deletions(-)
diff --git a/lib/detecter_tools/darknet/matrix.c b/lib/detecter_tools/darknet/matrix.c
index 63fd9bc..715ee80 100644
--- a/lib/detecter_tools/darknet/matrix.c
+++ b/lib/detecter_tools/darknet/matrix.c
@@ -1,332 +1,332 @@
-#include "matrix.h"
-#include "utils.h"
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-#include <assert.h>
-#include <math.h>
-
-void free_matrix(matrix m)
-{
- int i;
- for(i = 0; i < m.rows; ++i) free(m.vals[i]);
- free(m.vals);
-}
-
-float matrix_topk_accuracy(matrix truth, matrix guess, int k)
-{
- int* indexes = (int*)xcalloc(k, sizeof(int));
- int n = truth.cols;
- int i,j;
- int correct = 0;
- for(i = 0; i < truth.rows; ++i){
- top_k(guess.vals[i], n, k, indexes);
- for(j = 0; j < k; ++j){
- int class_id = indexes[j];
- if(truth.vals[i][class_id]){
- ++correct;
- break;
- }
- }
- }
- free(indexes);
- return (float)correct/truth.rows;
-}
-
-void scale_matrix(matrix m, float scale)
-{
- int i,j;
- for(i = 0; i < m.rows; ++i){
- for(j = 0; j < m.cols; ++j){
- m.vals[i][j] *= scale;
- }
- }
-}
-
-matrix resize_matrix(matrix m, int size)
-{
- int i;
- if (m.rows == size) return m;
- if (m.rows < size) {
- m.vals = (float**)xrealloc(m.vals, size * sizeof(float*));
- for (i = m.rows; i < size; ++i) {
- m.vals[i] = (float*)xcalloc(m.cols, sizeof(float));
- }
- } else if (m.rows > size) {
- for (i = size; i < m.rows; ++i) {
- free(m.vals[i]);
- }
- m.vals = (float**)xrealloc(m.vals, size * sizeof(float*));
- }
- m.rows = size;
- return m;
-}
-
-void matrix_add_matrix(matrix from, matrix to)
-{
- assert(from.rows == to.rows && from.cols == to.cols);
- int i,j;
- for(i = 0; i < from.rows; ++i){
- for(j = 0; j < from.cols; ++j){
- to.vals[i][j] += from.vals[i][j];
- }
- }
-}
-
-matrix make_matrix(int rows, int cols)
-{
- int i;
- matrix m;
- m.rows = rows;
- m.cols = cols;
- m.vals = (float**)xcalloc(m.rows, sizeof(float*));
- for(i = 0; i < m.rows; ++i){
- m.vals[i] = (float*)xcalloc(m.cols, sizeof(float));
- }
- return m;
-}
-
-matrix hold_out_matrix(matrix *m, int n)
-{
- int i;
- matrix h;
- h.rows = n;
- h.cols = m->cols;
- h.vals = (float**)xcalloc(h.rows, sizeof(float*));
- for(i = 0; i < n; ++i){
- int index = rand()%m->rows;
- h.vals[i] = m->vals[index];
- m->vals[index] = m->vals[--(m->rows)];
- }
- return h;
-}
-
-float *pop_column(matrix *m, int c)
-{
- float* col = (float*)xcalloc(m->rows, sizeof(float));
- int i, j;
- for(i = 0; i < m->rows; ++i){
- col[i] = m->vals[i][c];
- for(j = c; j < m->cols-1; ++j){
- m->vals[i][j] = m->vals[i][j+1];
- }
- }
- --m->cols;
- return col;
-}
-
-matrix csv_to_matrix(char *filename)
-{
- FILE *fp = fopen(filename, "r");
- if(!fp) file_error(filename);
-
- matrix m;
- m.cols = -1;
-
- char *line;
-
- int n = 0;
- int size = 1024;
- m.vals = (float**)xcalloc(size, sizeof(float*));
- while((line = fgetl(fp))){
- if(m.cols == -1) m.cols = count_fields(line);
- if(n == size){
- size *= 2;
- m.vals = (float**)xrealloc(m.vals, size * sizeof(float*));
- }
- m.vals[n] = parse_fields(line, m.cols);
- free(line);
- ++n;
- }
- m.vals = (float**)xrealloc(m.vals, n * sizeof(float*));
- m.rows = n;
- return m;
-}
-
-void matrix_to_csv(matrix m)
-{
- int i, j;
-
- for(i = 0; i < m.rows; ++i){
- for(j = 0; j < m.cols; ++j){
- if(j > 0) printf(",");
- printf("%.17g", m.vals[i][j]);
- }
- printf("\n");
- }
-}
-
-void print_matrix(matrix m)
-{
- int i, j;
- printf("%d X %d Matrix:\n",m.rows, m.cols);
- printf(" __");
- for(j = 0; j < 16*m.cols-1; ++j) printf(" ");
- printf("__ \n");
-
- printf("| ");
- for(j = 0; j < 16*m.cols-1; ++j) printf(" ");
- printf(" |\n");
-
- for(i = 0; i < m.rows; ++i){
- printf("| ");
- for(j = 0; j < m.cols; ++j){
- printf("%15.7f ", m.vals[i][j]);
- }
- printf(" |\n");
- }
- printf("|__");
- for(j = 0; j < 16*m.cols-1; ++j) printf(" ");
- printf("__|\n");
-}
-
-
-matrix make_matrix(int rows, int cols);
-
-void copy(float *x, float *y, int n);
-float dist(float *x, float *y, int n);
-int *sample(int n);
-
-int closest_center(float *datum, matrix centers)
-{
- int j;
- int best = 0;
- float best_dist = dist(datum, centers.vals[best], centers.cols);
- for (j = 0; j < centers.rows; ++j) {
- float new_dist = dist(datum, centers.vals[j], centers.cols);
- if (new_dist < best_dist) {
- best_dist = new_dist;
- best = j;
- }
- }
- return best;
-}
-
-float dist_to_closest_center(float *datum, matrix centers)
-{
- int ci = closest_center(datum, centers);
- return dist(datum, centers.vals[ci], centers.cols);
-}
-
-int kmeans_expectation(matrix data, int *assignments, matrix centers)
-{
- int i;
- int converged = 1;
- for (i = 0; i < data.rows; ++i) {
- int closest = closest_center(data.vals[i], centers);
- if (closest != assignments[i]) converged = 0;
- assignments[i] = closest;
- }
- return converged;
-}
-
-void kmeans_maximization(matrix data, int *assignments, matrix centers)
-{
- matrix old_centers = make_matrix(centers.rows, centers.cols);
-
- int i, j;
- int *counts = (int*)xcalloc(centers.rows, sizeof(int));
- for (i = 0; i < centers.rows; ++i) {
- for (j = 0; j < centers.cols; ++j) {
- old_centers.vals[i][j] = centers.vals[i][j];
- centers.vals[i][j] = 0;
- }
- }
- for (i = 0; i < data.rows; ++i) {
- ++counts[assignments[i]];
- for (j = 0; j < data.cols; ++j) {
- centers.vals[assignments[i]][j] += data.vals[i][j];
- }
- }
- for (i = 0; i < centers.rows; ++i) {
- if (counts[i]) {
- for (j = 0; j < centers.cols; ++j) {
- centers.vals[i][j] /= counts[i];
- }
- }
- }
-
- for (i = 0; i < centers.rows; ++i) {
- for (j = 0; j < centers.cols; ++j) {
- if(centers.vals[i][j] == 0) centers.vals[i][j] = old_centers.vals[i][j];
- }
- }
- free(counts);
- free_matrix(old_centers);
-}
-
-
-
-void random_centers(matrix data, matrix centers) {
- int i;
- int *s = sample(data.rows);
- for (i = 0; i < centers.rows; ++i) {
- copy(data.vals[s[i]], centers.vals[i], data.cols);
- }
- free(s);
-}
-
-int *sample(int n)
-{
- int i;
- int* s = (int*)xcalloc(n, sizeof(int));
- for (i = 0; i < n; ++i) s[i] = i;
- for (i = n - 1; i >= 0; --i) {
- int swap = s[i];
- int index = rand() % (i + 1);
- s[i] = s[index];
- s[index] = swap;
- }
- return s;
-}
-
-float dist(float *x, float *y, int n)
-{
- //printf(" x0 = %f, x1 = %f, y0 = %f, y1 = %f \n", x[0], x[1], y[0], y[1]);
- float mw = (x[0] < y[0]) ? x[0] : y[0];
- float mh = (x[1] < y[1]) ? x[1] : y[1];
- float inter = mw*mh;
- float sum = x[0] * x[1] + y[0] * y[1];
- float un = sum - inter;
- float iou = inter / un;
- return 1 - iou;
-}
-
-void copy(float *x, float *y, int n)
-{
- int i;
- for (i = 0; i < n; ++i) y[i] = x[i];
-}
-
-model do_kmeans(matrix data, int k)
-{
- matrix centers = make_matrix(k, data.cols);
- int* assignments = (int*)xcalloc(data.rows, sizeof(int));
- //smart_centers(data, centers);
- random_centers(data, centers); // IoU = 67.31% after kmeans
-
- /*
- // IoU = 63.29%, anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
- centers.vals[0][0] = 10; centers.vals[0][1] = 13;
- centers.vals[1][0] = 16; centers.vals[1][1] = 30;
- centers.vals[2][0] = 33; centers.vals[2][1] = 23;
- centers.vals[3][0] = 30; centers.vals[3][1] = 61;
- centers.vals[4][0] = 62; centers.vals[4][1] = 45;
- centers.vals[5][0] = 59; centers.vals[5][1] = 119;
- centers.vals[6][0] = 116; centers.vals[6][1] = 90;
- centers.vals[7][0] = 156; centers.vals[7][1] = 198;
- centers.vals[8][0] = 373; centers.vals[8][1] = 326;
- */
-
- // range centers [min - max] using exp graph or Pyth example
- if (k == 1) kmeans_maximization(data, assignments, centers);
- int i;
- for(i = 0; i < 1000 && !kmeans_expectation(data, assignments, centers); ++i) {
- kmeans_maximization(data, assignments, centers);
- }
- printf("\n iterations = %d \n", i);
- model m;
- m.assignments = assignments;
- m.centers = centers;
- return m;
-}
+#include "matrix.h"
+#include "utils.h"
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <assert.h>
+#include <math.h>
+
+void free_matrix(matrix m)
+{
+ int i;
+ for(i = 0; i < m.rows; ++i) free(m.vals[i]);
+ free(m.vals);
+}
+
+float matrix_topk_accuracy(matrix truth, matrix guess, int k)
+{
+ int* indexes = (int*)xcalloc(k, sizeof(int));
+ int n = truth.cols;
+ int i,j;
+ int correct = 0;
+ for(i = 0; i < truth.rows; ++i){
+ top_k(guess.vals[i], n, k, indexes);
+ for(j = 0; j < k; ++j){
+ int class_id = indexes[j];
+ if(truth.vals[i][class_id]){
+ ++correct;
+ break;
+ }
+ }
+ }
+ free(indexes);
+ return (float)correct/truth.rows;
+}
+
+void scale_matrix(matrix m, float scale)
+{
+ int i,j;
+ for(i = 0; i < m.rows; ++i){
+ for(j = 0; j < m.cols; ++j){
+ m.vals[i][j] *= scale;
+ }
+ }
+}
+
+matrix resize_matrix(matrix m, int size)
+{
+ int i;
+ if (m.rows == size) return m;
+ if (m.rows < size) {
+ m.vals = (float**)xrealloc(m.vals, size * sizeof(float*));
+ for (i = m.rows; i < size; ++i) {
+ m.vals[i] = (float*)xcalloc(m.cols, sizeof(float));
+ }
+ } else if (m.rows > size) {
+ for (i = size; i < m.rows; ++i) {
+ free(m.vals[i]);
+ }
+ m.vals = (float**)xrealloc(m.vals, size * sizeof(float*));
+ }
+ m.rows = size;
+ return m;
+}
+
+void matrix_add_matrix(matrix from, matrix to)
+{
+ assert(from.rows == to.rows && from.cols == to.cols);
+ int i,j;
+ for(i = 0; i < from.rows; ++i){
+ for(j = 0; j < from.cols; ++j){
+ to.vals[i][j] += from.vals[i][j];
+ }
+ }
+}
+
+matrix make_matrix(int rows, int cols)
+{
+ int i;
+ matrix m;
+ m.rows = rows;
+ m.cols = cols;
+ m.vals = (float**)xcalloc(m.rows, sizeof(float*));
+ for(i = 0; i < m.rows; ++i){
+ m.vals[i] = (float*)xcalloc(m.cols, sizeof(float));
+ }
+ return m;
+}
+
+matrix hold_out_matrix(matrix *m, int n)
+{
+ int i;
+ matrix h;
+ h.rows = n;
+ h.cols = m->cols;
+ h.vals = (float**)xcalloc(h.rows, sizeof(float*));
+ for(i = 0; i < n; ++i){
+ int index = rand()%m->rows;
+ h.vals[i] = m->vals[index];
+ m->vals[index] = m->vals[--(m->rows)];
+ }
+ return h;
+}
+
+float *pop_column(matrix *m, int c)
+{
+ float* col = (float*)xcalloc(m->rows, sizeof(float));
+ int i, j;
+ for(i = 0; i < m->rows; ++i){
+ col[i] = m->vals[i][c];
+ for(j = c; j < m->cols-1; ++j){
+ m->vals[i][j] = m->vals[i][j+1];
+ }
+ }
+ --m->cols;
+ return col;
+}
+
+matrix csv_to_matrix(char *filename)
+{
+ FILE *fp = fopen(filename, "r");
+ if(!fp) file_error(filename);
+
+ matrix m;
+ m.cols = -1;
+
+ char *line;
+
+ int n = 0;
+ int size = 1024;
+ m.vals = (float**)xcalloc(size, sizeof(float*));
+ while((line = fgetl(fp))){
+ if(m.cols == -1) m.cols = count_fields(line);
+ if(n == size){
+ size *= 2;
+ m.vals = (float**)xrealloc(m.vals, size * sizeof(float*));
+ }
+ m.vals[n] = parse_fields(line, m.cols);
+ free(line);
+ ++n;
+ }
+ m.vals = (float**)xrealloc(m.vals, n * sizeof(float*));
+ m.rows = n;
+ return m;
+}
+
+void matrix_to_csv(matrix m)
+{
+ int i, j;
+
+ for(i = 0; i < m.rows; ++i){
+ for(j = 0; j < m.cols; ++j){
+ if(j > 0) printf(",");
+ printf("%.17g", m.vals[i][j]);
+ }
+ printf("\n");
+ }
+}
+
+void print_matrix(matrix m)
+{
+ int i, j;
+ printf("%d X %d Matrix:\n",m.rows, m.cols);
+ printf(" __");
+ for(j = 0; j < 16*m.cols-1; ++j) printf(" ");
+ printf("__ \n");
+
+ printf("| ");
+ for(j = 0; j < 16*m.cols-1; ++j) printf(" ");
+ printf(" |\n");
+
+ for(i = 0; i < m.rows; ++i){
+ printf("| ");
+ for(j = 0; j < m.cols; ++j){
+ printf("%15.7f ", m.vals[i][j]);
+ }
+ printf(" |\n");
+ }
+ printf("|__");
+ for(j = 0; j < 16*m.cols-1; ++j) printf(" ");
+ printf("__|\n");
+}
+
+
+matrix make_matrix(int rows, int cols);
+
+void copy(float *x, float *y, int n);
+float dist(float *x, float *y, int n);
+int *sample(int n);
+
+int closest_center(float *datum, matrix centers)
+{
+ int j;
+ int best = 0;
+ float best_dist = dist(datum, centers.vals[best], centers.cols);
+ for (j = 0; j < centers.rows; ++j) {
+ float new_dist = dist(datum, centers.vals[j], centers.cols);
+ if (new_dist < best_dist) {
+ best_dist = new_dist;
+ best = j;
+ }
+ }
+ return best;
+}
+
+float dist_to_closest_center(float *datum, matrix centers)
+{
+ int ci = closest_center(datum, centers);
+ return dist(datum, centers.vals[ci], centers.cols);
+}
+
+int kmeans_expectation(matrix data, int *assignments, matrix centers)
+{
+ int i;
+ int converged = 1;
+ for (i = 0; i < data.rows; ++i) {
+ int closest = closest_center(data.vals[i], centers);
+ if (closest != assignments[i]) converged = 0;
+ assignments[i] = closest;
+ }
+ return converged;
+}
+
+void kmeans_maximization(matrix data, int *assignments, matrix centers)
+{
+ matrix old_centers = make_matrix(centers.rows, centers.cols);
+
+ int i, j;
+ int *counts = (int*)xcalloc(centers.rows, sizeof(int));
+ for (i = 0; i < centers.rows; ++i) {
+ for (j = 0; j < centers.cols; ++j) {
+ old_centers.vals[i][j] = centers.vals[i][j];
+ centers.vals[i][j] = 0;
+ }
+ }
+ for (i = 0; i < data.rows; ++i) {
+ ++counts[assignments[i]];
+ for (j = 0; j < data.cols; ++j) {
+ centers.vals[assignments[i]][j] += data.vals[i][j];
+ }
+ }
+ for (i = 0; i < centers.rows; ++i) {
+ if (counts[i]) {
+ for (j = 0; j < centers.cols; ++j) {
+ centers.vals[i][j] /= counts[i];
+ }
+ }
+ }
+
+ for (i = 0; i < centers.rows; ++i) {
+ for (j = 0; j < centers.cols; ++j) {
+ if(centers.vals[i][j] == 0) centers.vals[i][j] = old_centers.vals[i][j];
+ }
+ }
+ free(counts);
+ free_matrix(old_centers);
+}
+
+
+
+void random_centers(matrix data, matrix centers) {
+ int i;
+ int *s = sample(data.rows);
+ for (i = 0; i < centers.rows; ++i) {
+ copy(data.vals[s[i]], centers.vals[i], data.cols);
+ }
+ free(s);
+}
+
+int *sample(int n)
+{
+ int i;
+ int* s = (int*)xcalloc(n, sizeof(int));
+ for (i = 0; i < n; ++i) s[i] = i;
+ for (i = n - 1; i >= 0; --i) {
+ int swap = s[i];
+ int index = rand() % (i + 1);
+ s[i] = s[index];
+ s[index] = swap;
+ }
+ return s;
+}
+
+float dist(float *x, float *y, int n)
+{
+ //printf(" x0 = %f, x1 = %f, y0 = %f, y1 = %f \n", x[0], x[1], y[0], y[1]);
+ float mw = (x[0] < y[0]) ? x[0] : y[0];
+ float mh = (x[1] < y[1]) ? x[1] : y[1];
+ float inter = mw*mh;
+ float sum = x[0] * x[1] + y[0] * y[1];
+ float un = sum - inter;
+ float iou = inter / un;
+ return 1 - iou;
+}
+
+void copy(float *x, float *y, int n)
+{
+ int i;
+ for (i = 0; i < n; ++i) y[i] = x[i];
+}
+
+model do_kmeans(matrix data, int k)
+{
+ matrix centers = make_matrix(k, data.cols);
+ int* assignments = (int*)xcalloc(data.rows, sizeof(int));
+ //smart_centers(data, centers);
+ random_centers(data, centers); // IoU = 67.31% after kmeans
+
+ /*
+ // IoU = 63.29%, anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
+ centers.vals[0][0] = 10; centers.vals[0][1] = 13;
+ centers.vals[1][0] = 16; centers.vals[1][1] = 30;
+ centers.vals[2][0] = 33; centers.vals[2][1] = 23;
+ centers.vals[3][0] = 30; centers.vals[3][1] = 61;
+ centers.vals[4][0] = 62; centers.vals[4][1] = 45;
+ centers.vals[5][0] = 59; centers.vals[5][1] = 119;
+ centers.vals[6][0] = 116; centers.vals[6][1] = 90;
+ centers.vals[7][0] = 156; centers.vals[7][1] = 198;
+ centers.vals[8][0] = 373; centers.vals[8][1] = 326;
+ */
+
+ // range centers [min - max] using exp graph or Pyth example
+ if (k == 1) kmeans_maximization(data, assignments, centers);
+ int i;
+ for(i = 0; i < 1000 && !kmeans_expectation(data, assignments, centers); ++i) {
+ kmeans_maximization(data, assignments, centers);
+ }
+ printf("\n iterations = %d \n", i);
+ model m;
+ m.assignments = assignments;
+ m.centers = centers;
+ return m;
+}
--
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