From 913d355ec1cf34aad71fdd75202fc3b0309e63a0 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 28 Jan 2016 20:30:38 +0000
Subject: [PATCH] lots of stuff
---
src/classifier.c | 170 ++++++++++++++++++++++++++++++++++++++++++++++++++++----
1 files changed, 157 insertions(+), 13 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index 8a3ae5a..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"
@@ -143,7 +144,7 @@
clock_t time;
float avg_acc = 0;
float avg_topk = 0;
- int splits = 50;
+ int splits = m/1000;
int num = (i+1)*m/splits - i*m/splits;
data val, buffer;
@@ -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);
@@ -201,7 +341,7 @@
int i = 0;
char **names = get_labels(name_list);
clock_t time;
- int indexes[10];
+ int *indexes = calloc(top, sizeof(int));
char buff[256];
char *input = buff;
while(1){
@@ -214,7 +354,7 @@
if(!input) return;
strtok(input, "\n");
}
- image im = load_image_color(input, 256, 256);
+ image im = load_image_color(input, net.w, net.h);
float *X = im.data;
time=clock();
float *predictions = network_predict(net, X);
@@ -229,10 +369,10 @@
}
}
-void test_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int target_layer)
+void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer)
{
int curr = 0;
- network net = parse_network_cfg(filename);
+ network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
@@ -241,10 +381,8 @@
list *options = read_data_cfg(datacfg);
char *test_list = option_find_str(options, "test", "data/test.list");
- char *label_list = option_find_str(options, "labels", "data/labels.list");
int classes = option_find_int(options, "classes", 2);
- char **labels = get_labels(label_list);
list *plist = get_paths(test_list);
char **paths = (char **)list_to_array(plist);
@@ -262,7 +400,7 @@
args.classes = classes;
args.n = net.batch;
args.m = 0;
- args.labels = labels;
+ args.labels = 0;
args.d = &buffer;
args.type = CLASSIFICATION_DATA;
@@ -283,18 +421,22 @@
time=clock();
matrix pred = network_predict_data(net, val);
- int i;
+ int i, j;
if (target_layer >= 0){
//layer l = net.layers[target_layer];
}
- for(i = 0; i < val.X.rows; ++i){
-
+ for(i = 0; i < pred.rows; ++i){
+ printf("%s", paths[curr-net.batch+i]);
+ for(j = 0; j < pred.cols; ++j){
+ printf("\t%g", pred.vals[i][j]);
+ }
+ printf("\n");
}
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);
}
}
@@ -315,8 +457,10 @@
int layer = layer_s ? atoi(layer_s) : -1;
if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename);
else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights);
- else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights,filename, layer);
+ 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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