From 55fbdd1007488b2e98dbe4b07cc798954b5e165f Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 23 Sep 2015 06:29:03 +0000
Subject: [PATCH] fixes to image
---
src/writing.c | 145 ++++++++++++++++++++++++++++++++++++------------
1 files changed, 108 insertions(+), 37 deletions(-)
diff --git a/src/writing.c b/src/writing.c
index cfbc5fd..71dd53b 100644
--- a/src/writing.c
+++ b/src/writing.c
@@ -2,8 +2,13 @@
#include "utils.h"
#include "parser.h"
+#ifdef OPENCV
+#include "opencv2/highgui/highgui_c.h"
+#endif
+
void train_writing(char *cfgfile, char *weightfile)
{
+ char *backup_directory = "/home/pjreddie/backup/";
data_seed = time(0);
srand(time(0));
float avg_loss = -1;
@@ -14,59 +19,125 @@
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;
+ int imgs = net.batch*net.subdivisions;
list *plist = get_paths("figures.list");
char **paths = (char **)list_to_array(plist);
- printf("%d\n", plist->size);
clock_t time;
- while(1){
- ++i;
+ int N = plist->size;
+ printf("N: %d\n", N);
+
+ data train, buffer;
+
+ load_args args = {0};
+ args.w = net.w;
+ args.h = net.h;
+ args.paths = paths;
+ args.n = imgs;
+ args.m = N;
+ args.downsample = 1;
+ args.d = &buffer;
+ args.type = WRITING_DATA;
+
+ pthread_t load_thread = load_data_in_thread(args);
+ int epoch = (*net.seen)/N;
+ while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
time=clock();
- data train = load_data_writing(paths, imgs, plist->size, 512, 512);
+ pthread_join(load_thread, 0);
+ train = buffer;
+ load_thread = load_data_in_thread(args);
+ printf("Loaded %lf seconds\n",sec(clock()-time));
+
+ time=clock();
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);
*/
+ /*
+ 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);
+ */
+
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);
- }
+ printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
+ free_data(train);
+ if(get_current_batch(net)%100 == 0){
+ char buff[256];
+ sprintf(buff, "%s/%s_batch_%d.weights", backup_directory, base, get_current_batch(net));
+ save_weights(net, buff);
+ }
+ if(*net.seen/N > epoch){
+ epoch = *net.seen/N;
+ char buff[256];
+ sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+ save_weights(net, buff);
+ }
}
}
+void test_writing(char *cfgfile, char *weightfile, char *outfile)
+{
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ srand(2222222);
+ clock_t time;
+ char filename[256];
+
+ fgets(filename, 256, stdin);
+ strtok(filename, "\n");
+ image im = load_image_color(filename, 0, 0);
+ //image im = load_image_color("/home/pjreddie/darknet/data/figs/C02-1001-Figure-1.png", 0, 0);
+ image sized = resize_image(im, net.w, net.h);
+ printf("%d %d %d\n", im.h, im.w, im.c);
+ float *X = sized.data;
+ time=clock();
+ network_predict(net, X);
+ printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
+ image pred = get_network_image(net);
+
+ image t = threshold_image(pred, .5);
+ free_image(pred);
+ pred = t;
+
+ if (outfile) {
+ printf("Save image as %s.png (shape: %d %d)\n", outfile, pred.w, pred.h);
+ save_image(pred, outfile);
+ } else {
+ show_image(sized, "orig");
+ show_image(pred, "prediction");
+#ifdef OPENCV
+ cvWaitKey(0);
+ cvDestroyAllWindows();
+#endif
+ }
+
+ free_image(im);
+ free_image(sized);
+}
+
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;
- }
+ 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);
+ char *cfg = argv[3];
+ char *weights = (argc > 4) ? argv[4] : 0;
+ char *outfile = (argc > 5) ? argv[5] : 0;
+ if(0==strcmp(argv[2], "train")) train_writing(cfg, weights);
+ else if(0==strcmp(argv[2], "test")) test_writing(cfg, weights, outfile);
}
--
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