From e63b3a6f912cc2b1f6f00f2a9d342624a06dc3a4 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 09 Jun 2015 18:17:46 +0000
Subject: [PATCH] syncing messed something up
---
src/detection.c | 119 +++++++++++++++++++++++++----
src/writing.c | 73 ++++++++++++++++++
src/imagenet.c | 2
src/network_kernels.cu | 4
cfg/rescore.cfg | 8 +
src/data.c | 4
cfg/detection.cfg | 7 +
src/detection_layer.c | 5 -
8 files changed, 195 insertions(+), 27 deletions(-)
diff --git a/cfg/detection.cfg b/cfg/detection.cfg
index d08d2af..3f3b50e 100644
--- a/cfg/detection.cfg
+++ b/cfg/detection.cfg
@@ -178,6 +178,13 @@
filters=1024
activation=ramp
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=ramp
+
[connected]
output=4096
activation=ramp
diff --git a/cfg/rescore.cfg b/cfg/rescore.cfg
index 9024d53..954c158 100644
--- a/cfg/rescore.cfg
+++ b/cfg/rescore.cfg
@@ -178,6 +178,13 @@
filters=1024
activation=ramp
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=ramp
+
[connected]
output=4096
activation=ramp
@@ -195,4 +202,3 @@
rescore=1
nuisance = 0
background=0
-
diff --git a/src/data.c b/src/data.c
index ca5f4a6..425d216 100644
--- a/src/data.c
+++ b/src/data.c
@@ -527,11 +527,11 @@
data load_data_writing(char **paths, int n, int m, int w, int h)
{
if(m) paths = get_random_paths(paths, n, m);
- char **replace_paths = find_replace_paths(paths, n, ".png", "label.png");
+ char **replace_paths = find_replace_paths(paths, n, ".png", "-label.png");
data d;
d.shallow = 0;
d.X = load_image_paths(paths, n, w, h);
- d.y = load_image_paths_gray(replace_paths, n, w/4, h/4);
+ d.y = load_image_paths_gray(replace_paths, n, w/8, h/8);
if(m) free(paths);
int i;
for(i = 0; i < n; ++i) free(replace_paths[i]);
diff --git a/src/detection.c b/src/detection.c
index c012848..ccd5097 100644
--- a/src/detection.c
+++ b/src/detection.c
@@ -21,7 +21,7 @@
//printf("%d\n", j);
//printf("Prob: %f\n", box[j]);
int class = max_index(box+j, classes);
- if(box[j+class] > .4){
+ if(box[j+class] > .05){
//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]);
@@ -32,8 +32,8 @@
//float maxheight = distance_from_edge(r, side);
//float maxwidth = distance_from_edge(c, side);
j += classes;
- float y = box[j+0];
- float x = box[j+1];
+ float x = box[j+0];
+ float y = box[j+1];
x = (x+c)/side;
y = (y+r)/side;
float w = box[j+2]; //*maxwidth;
@@ -257,10 +257,11 @@
if (imgnet){
plist = get_paths("/home/pjreddie/data/imagenet/det.train.list");
}else{
- plist = get_paths("/home/pjreddie/data/voc/no_2012_val.txt");
+ //plist = get_paths("/home/pjreddie/data/voc/no_2012_val.txt");
//plist = get_paths("/home/pjreddie/data/voc/no_2007_test.txt");
+ //plist = get_paths("/home/pjreddie/data/voc/val_2012.txt");
//plist = get_paths("/home/pjreddie/data/coco/trainval.txt");
- //plist = get_paths("/home/pjreddie/data/voc/all2007-2012.txt");
+ plist = get_paths("/home/pjreddie/data/voc/all2007-2012.txt");
}
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);
@@ -272,12 +273,13 @@
train = buffer;
load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
- /*
+/*
image im = float_to_image(net.w, net.h, 3, train.X.vals[114]);
image copy = copy_image(im);
- draw_detection(copy, train.y.vals[114], 7);
+ draw_detection(copy, train.y.vals[114], 7, "truth");
+ cvWaitKey(0);
free_image(copy);
- */
+ */
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
@@ -289,7 +291,7 @@
if(i == 100){
net.learning_rate *= 10;
}
- if(i%100==0){
+ if(i%1000==0){
char buff[256];
sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
save_weights(net, buff);
@@ -313,8 +315,8 @@
int ci = k+classes+background+nuisance;
float x = (pred.vals[j][ci + 0] + col)/num_boxes;
float y = (pred.vals[j][ci + 1] + row)/num_boxes;
- float w = pred.vals[j][ci + 2]; //* distance_from_edge(row, num_boxes);
- float h = pred.vals[j][ci + 3]; //* distance_from_edge(col, num_boxes);
+ float w = pred.vals[j][ci + 2]; // distance_from_edge(row, num_boxes);
+ float h = pred.vals[j][ci + 3]; // distance_from_edge(col, num_boxes);
w = pow(w, 2);
h = pow(h, 2);
float prob = scale*pred.vals[j][k+class+background+nuisance];
@@ -337,7 +339,88 @@
srand(time(0));
//list *plist = get_paths("/home/pjreddie/data/voc/test_2007.txt");
- list *plist = get_paths("/home/pjreddie/data/voc/val_2012.txt");
+ //list *plist = get_paths("/home/pjreddie/data/voc/val_2012.txt");
+ list *plist = get_paths("/home/pjreddie/data/voc/test.txt");
+ //list *plist = get_paths("/home/pjreddie/data/voc/val.expanded.txt");
+ //list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
+ char **paths = (char **)list_to_array(plist);
+
+ int classes = layer.classes;
+ int nuisance = layer.nuisance;
+ int background = (layer.background && !nuisance);
+ int num_boxes = sqrt(get_detection_layer_locations(layer));
+
+ int per_box = 4+classes+background+nuisance;
+ int num_output = num_boxes*num_boxes*per_box;
+
+ int m = plist->size;
+ int i = 0;
+ int splits = 100;
+
+ int nthreads = 4;
+ int t;
+ data *val = calloc(nthreads, sizeof(data));
+ data *buf = calloc(nthreads, sizeof(data));
+ pthread_t *thr = calloc(nthreads, sizeof(data));
+
+ time_t start = time(0);
+
+ for(t = 0; t < nthreads; ++t){
+ int num = (i+1+t)*m/splits - (i+t)*m/splits;
+ char **part = paths+((i+t)*m/splits);
+ thr[t] = load_data_thread(part, num, 0, 0, num_output, net.w, net.h, &(buf[t]));
+ }
+
+ //clock_t time;
+ for(i = nthreads; i <= splits; i += nthreads){
+ //time=clock();
+ for(t = 0; t < nthreads; ++t){
+ pthread_join(thr[t], 0);
+ val[t] = buf[t];
+ }
+ for(t = 0; t < nthreads && i < splits; ++t){
+ int num = (i+1+t)*m/splits - (i+t)*m/splits;
+ char **part = paths+((i+t)*m/splits);
+ thr[t] = load_data_thread(part, num, 0, 0, num_output, net.w, net.h, &(buf[t]));
+ }
+
+ //fprintf(stderr, "%d: Loaded: %lf seconds\n", i, sec(clock()-time));
+ fprintf(stderr, "%d\n", i);
+ for(t = 0; t < nthreads; ++t){
+ predict_detections(net, val[t], .001, (i-nthreads+t)*m/splits, classes, nuisance, background, num_boxes, per_box);
+ free_data(val[t]);
+ }
+ }
+ fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
+}
+
+void do_mask(network net, data d, int offset, int classes, int nuisance, int background, int num_boxes, int per_box)
+{
+ matrix pred = network_predict_data(net, d);
+ int j, k;
+ for(j = 0; j < pred.rows; ++j){
+ printf("%d ", offset + j);
+ for(k = 0; k < pred.cols; k += per_box){
+ float scale = 1.-pred.vals[j][k];
+ printf("%f ", scale);
+ }
+ printf("\n");
+ }
+ free_matrix(pred);
+}
+
+void mask_detection(char *cfgfile, char *weightfile)
+{
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ 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/test_2007.txt");
+ //list *plist = get_paths("/home/pjreddie/data/voc/val_2012.txt");
//list *plist = get_paths("/home/pjreddie/data/voc/test.txt");
//list *plist = get_paths("/home/pjreddie/data/voc/val.expanded.txt");
//list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
@@ -381,7 +464,7 @@
fprintf(stderr, "%d: Loaded: %lf seconds\n", i, sec(clock()-time));
for(t = 0; t < nthreads; ++t){
- predict_detections(net, val[t], .01, (i-nthreads+t)*m/splits, classes, nuisance, background, num_boxes, per_box);
+ do_mask(net, val[t], (i-nthreads+t)*m/splits, classes, nuisance, background, num_boxes, per_box);
free_data(val[t]);
}
time=clock();
@@ -526,14 +609,17 @@
while(1){
fgets(filename, 256, stdin);
strtok(filename, "\n");
- image im = load_image_color(filename, im_size, im_size);
+ image im = load_image_color(filename,0,0);
+ image sized = resize_image(im, im_size, im_size);
printf("%d %d %d\n", im.h, im.w, im.c);
- float *X = im.data;
+ 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, "detections");
+ draw_detection(im, predictions, 7, "YOLO#SWAG#BLAZEIT");
free_image(im);
+ free_image(sized);
+ cvWaitKey(0);
}
}
@@ -551,5 +637,6 @@
else if(0==strcmp(argv[2], "teststuff")) train_detection_teststuff(cfg, weights);
else if(0==strcmp(argv[2], "trainloc")) train_localization(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_detection(cfg, weights);
+ else if(0==strcmp(argv[2], "mask")) mask_detection(cfg, weights);
else if(0==strcmp(argv[2], "validpost")) validate_detection_post(cfg, weights);
}
diff --git a/src/detection_layer.c b/src/detection_layer.c
index ae5930f..fcae7f3 100644
--- a/src/detection_layer.c
+++ b/src/detection_layer.c
@@ -372,15 +372,12 @@
l.delta[j+1] = 4 * (state.truth[j+1] - l.output[j+1]);
l.delta[j+2] = 4 * (state.truth[j+2] - l.output[j+2]);
l.delta[j+3] = 4 * (state.truth[j+3] - l.output[j+3]);
- if(1){
+ if(0){
for (j = offset; j < offset+classes; ++j) {
if(state.truth[j]) state.truth[j] = iou;
l.delta[j] = state.truth[j] - l.output[j];
}
}
-
- /*
- */
}
printf("Avg IOU: %f\n", avg_iou/count);
}
diff --git a/src/imagenet.c b/src/imagenet.c
index 2e1b685..9925a9a 100644
--- a/src/imagenet.c
+++ b/src/imagenet.c
@@ -32,7 +32,7 @@
pthread_join(load_thread, 0);
train = buffer;
-/*
+ /*
image im = float_to_image(256, 256, 3, train.X.vals[114]);
show_image(im, "training");
cvWaitKey(0);
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index 2ca2e2d..5e353ae 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -133,20 +133,18 @@
float *get_network_output_layer_gpu(network net, int i)
{
layer l = net.layers[i];
+ cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
if(l.type == CONVOLUTIONAL){
return l.output;
} else if(l.type == DECONVOLUTIONAL){
return l.output;
} else if(l.type == CONNECTED){
- cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
return l.output;
} else if(l.type == DETECTION){
- cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
return l.output;
} else if(l.type == MAXPOOL){
return l.output;
} else if(l.type == SOFTMAX){
- pull_softmax_layer_output(l);
return l.output;
}
return 0;
diff --git a/src/writing.c b/src/writing.c
new file mode 100644
index 0000000..1c1684b
--- /dev/null
+++ b/src/writing.c
@@ -0,0 +1,73 @@
+#include "network.h"
+#include "utils.h"
+#include "parser.h"
+
+void train_writing(char *cfgfile, char *weightfile)
+{
+ data_seed = time(0);
+ srand(time(0));
+ float avg_loss = -1;
+ char *base = basecfg(cfgfile);
+ printf("%s\n", base);
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ int imgs = 1024;
+ int i = net.seen/imgs;
+ list *plist = get_paths("figures.list");
+ char **paths = (char **)list_to_array(plist);
+ printf("%d\n", plist->size);
+ clock_t time;
+ while(1){
+ ++i;
+ time=clock();
+ data train = load_data_writing(paths, imgs, plist->size, 512, 512);
+ float loss = train_network(net, train);
+ #ifdef GPU
+ float *out = get_network_output_gpu(net);
+ #else
+ float *out = get_network_output(net);
+ #endif
+ image pred = float_to_image(64, 64, 1, out);
+ print_image(pred);
+
+/*
+ image im = float_to_image(256, 256, 3, train.X.vals[0]);
+ image lab = float_to_image(64, 64, 1, train.y.vals[0]);
+ image pred = float_to_image(64, 64, 1, out);
+ show_image(im, "image");
+ show_image(lab, "label");
+ print_image(lab);
+ show_image(pred, "pred");
+ cvWaitKey(0);
+ */
+
+ net.seen += imgs;
+ if(avg_loss == -1) 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), net.seen);
+ free_data(train);
+ if((i % 20000) == 0) net.learning_rate *= .1;
+ //if(i%100 == 0 && net.learning_rate > .00001) net.learning_rate *= .97;
+ if(i%1000==0){
+ char buff[256];
+ sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
+ save_weights(net, buff);
+ }
+ }
+}
+
+void run_writing(int argc, char **argv)
+{
+ if(argc < 4){
+ fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+ return;
+ }
+
+ char *cfg = argv[3];
+ char *weights = (argc > 4) ? argv[4] : 0;
+ if(0==strcmp(argv[2], "train")) train_writing(cfg, weights);
+}
+
--
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