From d0b9326a352ed2fbc3ae66fdef40b4533a2f211d Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 11 Aug 2015 06:22:27 +0000
Subject: [PATCH] Hacks to get nightmare to not break gridsizing
---
src/detection.c | 302 +++++++++++++++++++++++++++++++++----------------
1 files changed, 202 insertions(+), 100 deletions(-)
diff --git a/src/detection.c b/src/detection.c
index fa8b38c..f595701 100644
--- a/src/detection.c
+++ b/src/detection.c
@@ -1,96 +1,174 @@
#include "network.h"
+#include "detection_layer.h"
+#include "cost_layer.h"
#include "utils.h"
#include "parser.h"
+#include "box.h"
char *class_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
-#define AMNT 3
-void draw_detection(image im, float *box, int side)
+
+void draw_detection(image im, float *box, int side, int objectness, char *label)
{
int classes = 20;
- int elems = 4+classes;
+ int elems = 4+classes+objectness;
int j;
int r, c;
for(r = 0; r < side; ++r){
for(c = 0; c < side; ++c){
j = (r*side + c) * elems;
- //printf("%d\n", j);
- //printf("Prob: %f\n", box[j]);
+ float scale = 1;
+ if(objectness) scale = 1 - box[j++];
int class = max_index(box+j, classes);
- if(box[j+class] > .02 || 1){
- //int z;
- //for(z = 0; z < classes; ++z) printf("%f %s\n", box[j+z], class_names[z]);
- printf("%f %s\n", box[j+class], class_names[class]);
+ if(scale * box[j+class] > 0.2){
+ int width = box[j+class]*5 + 1;
+ printf("%f %s\n", scale * box[j+class], class_names[class]);
float red = get_color(0,class,classes);
float green = get_color(1,class,classes);
float blue = get_color(2,class,classes);
j += classes;
- int d = im.w/side;
- int y = r*d+box[j]*d;
- int x = c*d+box[j+1]*d;
- int h = box[j+2]*im.h;
- int w = box[j+3]*im.w;
- draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2,red,green,blue);
+ float x = box[j+0];
+ float y = box[j+1];
+ x = (x+c)/side;
+ y = (y+r)/side;
+ float w = box[j+2]; //*maxwidth;
+ float h = box[j+3]; //*maxheight;
+ h = h*h;
+ w = w*w;
+
+ int left = (x-w/2)*im.w;
+ int right = (x+w/2)*im.w;
+ int top = (y-h/2)*im.h;
+ int bot = (y+h/2)*im.h;
+ draw_box_width(im, left, top, right, bot, width, red, green, blue);
}
}
}
- //printf("Done\n");
- show_image(im, "box");
- cvWaitKey(0);
+ show_image(im, label);
}
void train_detection(char *cfgfile, char *weightfile)
{
+ char *train_images = "/home/pjreddie/data/voc/test/train.txt";
+ char *backup_directory = "/home/pjreddie/backup/";
+ srand(time(0));
+ data_seed = time(0);
char *base = basecfg(cfgfile);
printf("%s\n", base);
- float avg_loss = 1;
+ float avg_loss = -1;
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
+ detection_layer layer = get_network_detection_layer(net);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 128;
- srand(time(0));
- //srand(23410);
int i = net.seen/imgs;
- list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
- char **paths = (char **)list_to_array(plist);
- printf("%d\n", plist->size);
data train, buffer;
- int im_dim = 512;
- int jitter = 64;
- int classes = 21;
- pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, &buffer);
+
+ int classes = layer.classes;
+ int background = layer.objectness;
+ int side = sqrt(get_detection_layer_locations(layer));
+
+ char **paths;
+ list *plist = get_paths(train_images);
+ int N = plist->size;
+
+ paths = (char **)list_to_array(plist);
+ pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
clock_t time;
- while(1){
+ while(i*imgs < N*130){
i += 1;
time=clock();
pthread_join(load_thread, 0);
train = buffer;
- load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, &buffer);
-
- /*
- image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[0]);
- draw_detection(im, train.y.vals[0], 7);
- show_image(im, "truth");
- cvWaitKey(0);
- */
+ load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network(net, train);
net.seen += imgs;
+ if (avg_loss < 0) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
+
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
- if(i%100==0){
+ if((i-1)*imgs <= N && i*imgs > N){
+ fprintf(stderr, "First stage done\n");
+ net.learning_rate *= 10;
char buff[256];
- sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
+ sprintf(buff, "%s/%s_first_stage.weights", backup_directory, base);
+ save_weights(net, buff);
+ }
+ if((i-1)*imgs <= 80*N && i*imgs > N*80){
+ fprintf(stderr, "Second stage done.\n");
+ net.learning_rate *= .1;
+ char buff[256];
+ sprintf(buff, "%s/%s_second_stage.weights", backup_directory, base);
+ save_weights(net, buff);
+ return;
+ }
+ if((i-1)*imgs <= 120*N && i*imgs > N*120){
+ fprintf(stderr, "Third stage done.\n");
+ char buff[256];
+ sprintf(buff, "%s/%s_third_stage.weights", backup_directory, base);
+ net.layers[net.n-1].rescore = 1;
+ save_weights(net, buff);
+ }
+ if(i%1000==0){
+ char buff[256];
+ sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
}
free_data(train);
}
+ char buff[256];
+ sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+ save_weights(net, buff);
+}
+
+void convert_detections(float *predictions, int classes, int objectness, int background, int num_boxes, int w, int h, float thresh, float **probs, box *boxes)
+{
+ int i,j;
+ int per_box = 4+classes+(background || objectness);
+ for (i = 0; i < num_boxes*num_boxes; ++i){
+ float scale = 1;
+ if(objectness) scale = 1-predictions[i*per_box];
+ int offset = i*per_box+(background||objectness);
+ for(j = 0; j < classes; ++j){
+ float prob = scale*predictions[offset+j];
+ probs[i][j] = (prob > thresh) ? prob : 0;
+ }
+ int row = i / num_boxes;
+ int col = i % num_boxes;
+ offset += classes;
+ boxes[i].x = (predictions[offset + 0] + col) / num_boxes * w;
+ boxes[i].y = (predictions[offset + 1] + row) / num_boxes * h;
+ boxes[i].w = pow(predictions[offset + 2], 2) * w;
+ boxes[i].h = pow(predictions[offset + 3], 2) * h;
+ }
+}
+
+void print_detections(FILE **fps, char *id, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
+{
+ int i, j;
+ for(i = 0; i < num_boxes*num_boxes; ++i){
+ float xmin = boxes[i].x - boxes[i].w/2.;
+ float xmax = boxes[i].x + boxes[i].w/2.;
+ float ymin = boxes[i].y - boxes[i].h/2.;
+ float ymax = boxes[i].y + boxes[i].h/2.;
+
+ if (xmin < 0) xmin = 0;
+ if (ymin < 0) ymin = 0;
+ if (xmax > w) xmax = w;
+ if (ymax > h) ymax = h;
+
+ for(j = 0; j < classes; ++j){
+ if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j],
+ xmin, ymin, xmax, ymax);
+ }
+ }
}
void validate_detection(char *cfgfile, char *weightfile)
@@ -99,89 +177,112 @@
if(weightfile){
load_weights(&net, weightfile);
}
+ set_batch_network(&net, 1);
+ detection_layer layer = get_network_detection_layer(net);
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
- list *plist = get_paths("/home/pjreddie/data/voc/val.txt");
+ char *base = "results/comp4_det_test_";
+ list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
char **paths = (char **)list_to_array(plist);
- int num_output = 1225;
- int im_size = 448;
- int classes = 21;
+
+ int classes = layer.classes;
+ int objectness = layer.objectness;
+ int background = layer.background;
+ int num_boxes = sqrt(get_detection_layer_locations(layer));
+
+ int j;
+ FILE **fps = calloc(classes, sizeof(FILE *));
+ for(j = 0; j < classes; ++j){
+ char buff[1024];
+ snprintf(buff, 1024, "%s%s.txt", base, class_names[j]);
+ fps[j] = fopen(buff, "w");
+ }
+ box *boxes = calloc(num_boxes*num_boxes, sizeof(box));
+ float **probs = calloc(num_boxes*num_boxes, sizeof(float *));
+ for(j = 0; j < num_boxes*num_boxes; ++j) probs[j] = calloc(classes, sizeof(float *));
int m = plist->size;
- int i = 0;
- int splits = 100;
- int num = (i+1)*m/splits - i*m/splits;
+ int i=0;
+ int t;
- fprintf(stderr, "%d\n", m);
- data val, buffer;
- pthread_t load_thread = load_data_thread(paths, num, 0, 0, num_output, im_size, im_size, &buffer);
- clock_t time;
- for(i = 1; i <= splits; ++i){
- time=clock();
- pthread_join(load_thread, 0);
- val = buffer;
+ float thresh = .001;
+ int nms = 1;
+ float iou_thresh = .5;
- num = (i+1)*m/splits - i*m/splits;
- char **part = paths+(i*m/splits);
- if(i != splits) load_thread = load_data_thread(part, num, 0, 0, num_output, im_size, im_size, &buffer);
-
- fprintf(stderr, "%d: Loaded: %lf seconds\n", i, sec(clock()-time));
- matrix pred = network_predict_data(net, val);
- int j, k, class;
- for(j = 0; j < pred.rows; ++j){
- for(k = 0; k < pred.cols; k += classes+4){
-
- /*
- int z;
- for(z = 0; z < 25; ++z) printf("%f, ", pred.vals[j][k+z]);
- printf("\n");
- */
-
- //if (pred.vals[j][k] > .001){
- for(class = 0; class < classes-1; ++class){
- int index = (k)/(classes+4);
- int r = index/7;
- int c = index%7;
- float y = (r + pred.vals[j][k+0+classes])/7.;
- float x = (c + pred.vals[j][k+1+classes])/7.;
- float h = pred.vals[j][k+2+classes];
- float w = pred.vals[j][k+3+classes];
- printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, pred.vals[j][k+class], y, x, h, w);
- }
- //}
- }
- }
-
- time=clock();
- free_data(val);
+ int nthreads = 8;
+ image *val = calloc(nthreads, sizeof(image));
+ image *val_resized = calloc(nthreads, sizeof(image));
+ image *buf = calloc(nthreads, sizeof(image));
+ image *buf_resized = calloc(nthreads, sizeof(image));
+ pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
+ for(t = 0; t < nthreads; ++t){
+ thr[t] = load_image_thread(paths[i+t], &buf[t], &buf_resized[t], net.w, net.h);
}
+ time_t start = time(0);
+ for(i = nthreads; i < m+nthreads; i += nthreads){
+ fprintf(stderr, "%d\n", i);
+ for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
+ pthread_join(thr[t], 0);
+ val[t] = buf[t];
+ val_resized[t] = buf_resized[t];
+ }
+ for(t = 0; t < nthreads && i+t < m; ++t){
+ thr[t] = load_image_thread(paths[i+t], &buf[t], &buf_resized[t], net.w, net.h);
+ }
+ for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
+ char *path = paths[i+t-nthreads];
+ char *id = basecfg(path);
+ float *X = val_resized[t].data;
+ float *predictions = network_predict(net, X);
+ int w = val[t].w;
+ int h = val[t].h;
+ convert_detections(predictions, classes, objectness, background, num_boxes, w, h, thresh, probs, boxes);
+ if (nms) do_nms(boxes, probs, num_boxes, classes, iou_thresh);
+ print_detections(fps, id, boxes, probs, num_boxes, classes, w, h);
+ free(id);
+ free_image(val[t]);
+ free_image(val_resized[t]);
+ }
+ }
+ fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
}
-void test_detection(char *cfgfile, char *weightfile)
+void test_detection(char *cfgfile, char *weightfile, char *filename)
{
+
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
- int im_size = 448;
+ detection_layer layer = get_network_detection_layer(net);
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
- char filename[256];
+ char input[256];
while(1){
- fgets(filename, 256, stdin);
- strtok(filename, "\n");
- image im = load_image_color(filename, im_size, im_size);
- translate_image(im, -128);
- scale_image(im, 1/128.);
- printf("%d %d %d\n", im.h, im.w, im.c);
- float *X = im.data;
+ if(filename){
+ strncpy(input, filename, 256);
+ } else {
+ printf("Enter Image Path: ");
+ fflush(stdout);
+ fgets(input, 256, stdin);
+ strtok(input, "\n");
+ }
+ image im = load_image_color(input,0,0);
+ image sized = resize_image(im, net.w, net.h);
+ float *X = sized.data;
time=clock();
float *predictions = network_predict(net, X);
- printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
- draw_detection(im, predictions, 7);
+ printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+ draw_detection(im, predictions, 7, layer.objectness, "predictions");
free_image(im);
+ free_image(sized);
+#ifdef OPENCV
+ cvWaitKey(0);
+ cvDestroyAllWindows();
+#endif
+ if (filename) break;
}
}
@@ -194,7 +295,8 @@
char *cfg = argv[3];
char *weights = (argc > 4) ? argv[4] : 0;
- if(0==strcmp(argv[2], "test")) test_detection(cfg, weights);
+ char *filename = (argc > 5) ? argv[5]: 0;
+ if(0==strcmp(argv[2], "test")) test_detection(cfg, weights, filename);
else if(0==strcmp(argv[2], "train")) train_detection(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_detection(cfg, weights);
}
--
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