From 5c067dc44785a761a0243d8cd634e3ac17d548ad Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 12 Sep 2016 20:55:20 +0000
Subject: [PATCH] good chance I didn't break anything
---
src/classifier.c | 206 ++++++++++++++++++++++++++++++++++++++++++++-------
1 files changed, 177 insertions(+), 29 deletions(-)
diff --git a/src/classifier.c b/src/classifier.c
index 7ab70e2..3424216 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -5,6 +5,7 @@
#include "blas.h"
#include "assert.h"
#include "classifier.h"
+#include "cuda.h"
#include <sys/time.h>
#ifdef OPENCV
@@ -51,6 +52,134 @@
return v;
}
+void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
+{
+#ifdef GPU
+ int nthreads = 8;
+ int i;
+
+ data_seed = time(0);
+ srand(time(0));
+ float avg_loss = -1;
+ char *base = basecfg(cfgfile);
+ printf("%s\n", base);
+ printf("%d\n", ngpus);
+ network *nets = calloc(ngpus, sizeof(network));
+ for(i = 0; i < ngpus; ++i){
+ cuda_set_device(gpus[i]);
+ nets[i] = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&(nets[i]), weightfile);
+ }
+ if(clear) *nets[i].seen = 0;
+ }
+ network net = nets[0];
+
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ int imgs = net.batch*ngpus/nthreads;
+ assert(net.batch*ngpus % nthreads == 0);
+
+ list *options = read_data_cfg(datacfg);
+
+ char *backup_directory = option_find_str(options, "backup", "/backup/");
+ char *label_list = option_find_str(options, "labels", "data/labels.list");
+ char *train_list = option_find_str(options, "train", "data/train.list");
+ int classes = option_find_int(options, "classes", 2);
+
+ char **labels = get_labels(label_list);
+ list *plist = get_paths(train_list);
+ char **paths = (char **)list_to_array(plist);
+ printf("%d\n", plist->size);
+ int N = plist->size;
+ clock_t time;
+
+ pthread_t *load_threads = calloc(nthreads, sizeof(pthread_t));
+ data *trains = calloc(nthreads, sizeof(data));
+ data *buffers = calloc(nthreads, sizeof(data));
+
+ load_args args = {0};
+ args.w = net.w;
+ args.h = net.h;
+
+ args.min = net.min_crop;
+ args.max = net.max_crop;
+ args.angle = net.angle;
+ args.aspect = net.aspect;
+ args.exposure = net.exposure;
+ args.saturation = net.saturation;
+ args.hue = net.hue;
+ args.size = net.w;
+
+ args.paths = paths;
+ args.classes = classes;
+ args.n = imgs;
+ args.m = N;
+ args.labels = labels;
+ args.type = CLASSIFICATION_DATA;
+
+ for(i = 0; i < nthreads; ++i){
+ args.d = buffers + i;
+ load_threads[i] = load_data_in_thread(args);
+ }
+
+ int epoch = (*net.seen)/N;
+ while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
+ time=clock();
+ for(i = 0; i < nthreads; ++i){
+ pthread_join(load_threads[i], 0);
+ trains[i] = buffers[i];
+ }
+ data train = concat_datas(trains, nthreads);
+
+ for(i = 0; i < nthreads; ++i){
+ args.d = buffers + i;
+ load_threads[i] = load_data_in_thread(args);
+ }
+
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+ time=clock();
+
+ float loss = train_networks(nets, ngpus, train);
+ if(avg_loss == -1) avg_loss = loss;
+ avg_loss = avg_loss*.9 + loss*.1;
+ 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);
+ for(i = 0; i < nthreads; ++i){
+ free_data(trains[i]);
+ }
+ 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);
+ }
+ if(get_current_batch(net)%100 == 0){
+ char buff[256];
+ sprintf(buff, "%s/%s.backup",backup_directory,base);
+ save_weights(net, buff);
+ }
+ }
+ char buff[256];
+ sprintf(buff, "%s/%s.weights", backup_directory, base);
+ save_weights(net, buff);
+
+ for(i = 0; i < nthreads; ++i){
+ pthread_join(load_threads[i], 0);
+ free_data(buffers[i]);
+ }
+ free(buffers);
+ free(trains);
+ free(load_threads);
+
+ free_network(net);
+ free_ptrs((void**)labels, classes);
+ free_ptrs((void**)paths, plist->size);
+ free_list(plist);
+ free(base);
+#endif
+}
+
+
void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
{
int nthreads = 8;
@@ -130,7 +259,7 @@
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
- #ifdef OPENCV
+#ifdef OPENCV
if(0){
int u;
for(u = 0; u < imgs; ++u){
@@ -139,7 +268,7 @@
cvWaitKey(0);
}
}
- #endif
+#endif
float loss = train_network(net, train);
if(avg_loss == -1) avg_loss = loss;
@@ -546,29 +675,29 @@
float *X = im.data;
time=clock();
float *predictions = network_predict(net, X);
-
+
layer l = net.layers[layer_num];
for(i = 0; i < l.c; ++i){
- if(l.rolling_mean) printf("%f %f %f\n", l.rolling_mean[i], l.rolling_variance[i], l.scales[i]);
+ if(l.rolling_mean) printf("%f %f %f\n", l.rolling_mean[i], l.rolling_variance[i], l.scales[i]);
}
- #ifdef GPU
+#ifdef GPU
cuda_pull_array(l.output_gpu, l.output, l.outputs);
- #endif
+#endif
for(i = 0; i < l.outputs; ++i){
printf("%f\n", l.output[i]);
}
/*
-
- printf("\n\nWeights\n");
- for(i = 0; i < l.n*l.size*l.size*l.c; ++i){
- printf("%f\n", l.filters[i]);
- }
- printf("\n\nBiases\n");
- for(i = 0; i < l.n; ++i){
- printf("%f\n", l.biases[i]);
- }
- */
+ printf("\n\nWeights\n");
+ for(i = 0; i < l.n*l.size*l.size*l.c; ++i){
+ printf("%f\n", l.filters[i]);
+ }
+
+ printf("\n\nBiases\n");
+ for(i = 0; i < l.n; ++i){
+ printf("%f\n", l.biases[i]);
+ }
+ */
top_predictions(net, top, indexes);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
@@ -794,15 +923,15 @@
if(!in.data) break;
image in_s = resize_image(in, net.w, net.h);
- image out = in;
- int x1 = out.w / 20;
- int y1 = out.h / 20;
- int x2 = 2*x1;
- int y2 = out.h - out.h/20;
+ image out = in;
+ int x1 = out.w / 20;
+ int y1 = out.h / 20;
+ int x2 = 2*x1;
+ int y2 = out.h - out.h/20;
- int border = .01*out.h;
- int h = y2 - y1 - 2*border;
- int w = x2 - x1 - 2*border;
+ int border = .01*out.h;
+ int h = y2 - y1 - 2*border;
+ int w = x2 - x1 - 2*border;
float *predictions = network_predict(net, in_s.data);
float curr_threat = predictions[0] * 0 + predictions[1] * .6 + predictions[2];
@@ -821,11 +950,11 @@
y1 + .02*h + 3*border, .5*border, 0,0,0);
draw_box_width(out, x2 + border, y1 + .42*h, x2 + .5 * w, y1 + .42*h + border, border, 0,0,0);
if(threat > .57) {
- draw_box_width(out, x2 + .5 * w + border,
- y1 + .42*h - 2*border,
- x2 + .5 * w + 6*border,
- y1 + .42*h + 3*border, 3*border, 1,1,0);
- }
+ draw_box_width(out, x2 + .5 * w + border,
+ y1 + .42*h - 2*border,
+ x2 + .5 * w + 6*border,
+ y1 + .42*h + 3*border, 3*border, 1,1,0);
+ }
draw_box_width(out, x2 + .5 * w + border,
y1 + .42*h - 2*border,
x2 + .5 * w + 6*border,
@@ -942,6 +1071,24 @@
return;
}
+ char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
+ int *gpus = 0;
+ int ngpus = 0;
+ if(gpu_list){
+ printf("%s\n", gpu_list);
+ int len = strlen(gpu_list);
+ ngpus = 1;
+ int i;
+ for(i = 0; i < len; ++i){
+ if (gpu_list[i] == ',') ++ngpus;
+ }
+ gpus = calloc(ngpus, sizeof(int));
+ for(i = 0; i < ngpus; ++i){
+ gpus[i] = atoi(gpu_list);
+ gpu_list = strchr(gpu_list, ',')+1;
+ }
+ }
+
int cam_index = find_int_arg(argc, argv, "-c", 0);
int clear = find_arg(argc, argv, "-clear");
char *data = argv[3];
@@ -953,6 +1100,7 @@
if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename);
else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s));
else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, clear);
+ else if(0==strcmp(argv[2], "trainm")) train_classifier_multi(data, cfg, weights, gpus, ngpus, clear);
else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename);
else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename);
else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer);
--
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