From 1578ec70d751231218c869d345404ea68be9e5e8 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 18 Jan 2016 23:40:14 +0000
Subject: [PATCH] idk
---
src/classifier.c | 144 +++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 143 insertions(+), 1 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index ddd88b1..9924c37 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -2,6 +2,7 @@
#include "utils.h"
#include "parser.h"
#include "option_list.h"
+#include "blas.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
@@ -183,6 +184,145 @@
}
}
+void validate_classifier_10(char *datacfg, char *filename, char *weightfile)
+{
+ int i, j;
+ network net = parse_network_cfg(filename);
+ set_batch_network(&net, 1);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "labels", "data/labels.list");
+ char *valid_list = option_find_str(options, "valid", "data/train.list");
+ int classes = option_find_int(options, "classes", 2);
+ int topk = option_find_int(options, "top", 1);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(valid_list);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ float avg_acc = 0;
+ float avg_topk = 0;
+ int *indexes = calloc(topk, sizeof(int));
+
+ for(i = 0; i < m; ++i){
+ int class = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class = j;
+ break;
+ }
+ }
+ image im = load_image_color(paths[i], 256, 256);
+ image images[10];
+ images[0] = crop_image(im, -16, -16, 256, 256);
+ images[1] = crop_image(im, 16, -16, 256, 256);
+ images[2] = crop_image(im, 0, 0, 256, 256);
+ images[3] = crop_image(im, -16, 16, 256, 256);
+ images[4] = crop_image(im, 16, 16, 256, 256);
+ flip_image(im);
+ images[5] = crop_image(im, -16, -16, 256, 256);
+ images[6] = crop_image(im, 16, -16, 256, 256);
+ images[7] = crop_image(im, 0, 0, 256, 256);
+ images[8] = crop_image(im, -16, 16, 256, 256);
+ images[9] = crop_image(im, 16, 16, 256, 256);
+ float *pred = calloc(classes, sizeof(float));
+ for(j = 0; j < 10; ++j){
+ float *p = network_predict(net, images[j].data);
+ axpy_cpu(classes, 1, p, 1, pred, 1);
+ free_image(images[j]);
+ }
+ free_image(im);
+ top_k(pred, classes, topk, indexes);
+ free(pred);
+ if(indexes[0] == class) avg_acc += 1;
+ for(j = 0; j < topk; ++j){
+ if(indexes[j] == class) avg_topk += 1;
+ }
+
+ printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+ }
+}
+
+void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
+{
+ int i, j;
+ network net = parse_network_cfg(filename);
+ set_batch_network(&net, 1);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ list *options = read_data_cfg(datacfg);
+
+ char *label_list = option_find_str(options, "labels", "data/labels.list");
+ char *valid_list = option_find_str(options, "valid", "data/train.list");
+ int classes = option_find_int(options, "classes", 2);
+ int topk = option_find_int(options, "top", 1);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(valid_list);
+ int scales[] = {224, 256, 384, 480, 640};
+ int nscales = sizeof(scales)/sizeof(scales[0]);
+
+ char **paths = (char **)list_to_array(plist);
+ int m = plist->size;
+ free_list(plist);
+
+ float avg_acc = 0;
+ float avg_topk = 0;
+ int *indexes = calloc(topk, sizeof(int));
+
+ for(i = 0; i < m; ++i){
+ int class = -1;
+ char *path = paths[i];
+ for(j = 0; j < classes; ++j){
+ if(strstr(path, labels[j])){
+ class = j;
+ break;
+ }
+ }
+ float *pred = calloc(classes, sizeof(float));
+ image im = load_image_color(paths[i], 0, 0);
+ for(j = 0; j < nscales; ++j){
+ int w, h;
+ if(im.w < im.h){
+ w = scales[j];
+ h = (im.h*w)/im.w;
+ } else {
+ h = scales[j];
+ w = (im.w * h) / im.h;
+ }
+ resize_network(&net, w, h);
+ image r = resize_image(im, w, h);
+ float *p = network_predict(net, r.data);
+ axpy_cpu(classes, 1, p, 1, pred, 1);
+ flip_image(r);
+ p = network_predict(net, r.data);
+ axpy_cpu(classes, 1, p, 1, pred, 1);
+ free_image(r);
+ }
+ free_image(im);
+ top_k(pred, classes, topk, indexes);
+ free(pred);
+ if(indexes[0] == class) avg_acc += 1;
+ for(j = 0; j < topk; ++j){
+ if(indexes[j] == class) avg_topk += 1;
+ }
+
+ printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+ }
+}
+
void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename)
{
network net = parse_network_cfg(cfgfile);
@@ -296,7 +436,7 @@
free_matrix(pred);
- fprintf(stderr, "%lf seconds, %d images\n", sec(clock()-time), val.X.rows);
+ fprintf(stderr, "%lf seconds, %d images, %d total\n", sec(clock()-time), val.X.rows, curr);
free_data(val);
}
}
@@ -319,6 +459,8 @@
else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights);
else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer);
else if(0==strcmp(argv[2], "valid")) validate_classifier(data, cfg, weights);
+ else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights);
+ else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights);
}
--
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