Added dice code

Makefile

@@ -1,5 +1,5 @@
-GPU=0
+GPU=1
-OPENCV=0
+OPENCV=1
 DEBUG=0
 
 ARCH= --gpu-architecture=compute_20 --gpu-code=compute_20
@@ -34,7 +34,7 @@ CFLAGS+= -DGPU
 LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
 endif
 
-OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o detection.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o
+OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o detection.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o
 ifeq ($(GPU), 1) 
 OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
 endif

scripts/dice_label.sh

@@ -0,0 +1,20 @@
+mkdir -p images
+mkdir -p images/orig
+mkdir -p images/train
+mkdir -p images/val
+
+ffmpeg -i Face1.mp4 images/orig/face1_%6d.jpg
+ffmpeg -i Face2.mp4 images/orig/face2_%6d.jpg
+ffmpeg -i Face3.mp4 images/orig/face3_%6d.jpg
+ffmpeg -i Face4.mp4 images/orig/face4_%6d.jpg
+ffmpeg -i Face5.mp4 images/orig/face5_%6d.jpg
+ffmpeg -i Face6.mp4 images/orig/face6_%6d.jpg
+
+mogrify -resize 100x100^ -gravity center -crop 100x100+0+0 +repage images/orig/*
+
+ls images/orig/* | shuf | head -n 1000 | xargs mv -t images/val
+mv images/orig/* images/train
+
+find `pwd`/images/train > dice.train.list -name \*.jpg
+find `pwd`/images/val > dice.val.list -name \*.jpg
+

src/darknet.c

@@ -15,6 +15,7 @@ extern void run_coco(int argc, char **argv);
 extern void run_writing(int argc, char **argv);
 extern void run_captcha(int argc, char **argv);
 extern void run_nightmare(int argc, char **argv);
+extern void run_dice(int argc, char **argv);
 
 void change_rate(char *filename, float scale, float add)
 {
@@ -115,6 +116,8 @@ int main(int argc, char **argv)
         run_detection(argc, argv);
     } else if (0 == strcmp(argv[1], "coco")){
         run_coco(argc, argv);
+    } else if (0 == strcmp(argv[1], "dice")){
+        run_dice(argc, argv);
     } else if (0 == strcmp(argv[1], "writing")){
         run_writing(argc, argv);
     } else if (0 == strcmp(argv[1], "test")){

src/dice.c

@@ -0,0 +1,118 @@
+#include "network.h"
+#include "utils.h"
+#include "parser.h"
+
+char *dice_labels[] = {"face1","face2","face3","face4","face5","face6"};
+
+void train_dice(char *cfgfile, char *weightfile)
+{
+    data_seed = time(0);
+    srand(time(0));
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    char *backup_directory = "/home/pjreddie/backup/";
+    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;
+    char **labels = dice_labels;
+    list *plist = get_paths("data/dice/dice.train.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(paths, imgs, plist->size, labels, 6, net.w, net.h);
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+        time=clock();
+        float loss = train_network(net, train);
+        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 % 100) == 0) net.learning_rate *= .1;
+        if(i%100==0){
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, i);
+            save_weights(net, buff);
+        }
+    }
+}
+
+void validate_dice(char *filename, char *weightfile)
+{
+    network net = parse_network_cfg(filename);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    srand(time(0));
+
+    char **labels = dice_labels;
+    list *plist = get_paths("data/dice/dice.val.list");
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    data val = load_data(paths, m, 0, labels, 6, net.w, net.h);
+    float *acc = network_accuracies(net, val);
+    printf("Validation Accuracy: %f, %d images\n", acc[0], m);
+    free_data(val);
+}
+
+void test_dice(char *cfgfile, char *weightfile, char *filename)
+{
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    set_batch_network(&net, 1);
+    srand(2222222);
+    int i = 0;
+    char **names = dice_labels;
+    char input[256];
+    int indexes[6];
+    while(1){
+        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, net.w, net.h);
+        float *X = im.data;
+        float *predictions = network_predict(net, X);
+        top_predictions(net, 6, indexes);
+        for(i = 0; i < 6; ++i){
+            int index = indexes[i];
+            printf("%s: %f\n", names[index], predictions[index]);
+        }
+        free_image(im);
+        if (filename) break;
+    }
+}
+
+void run_dice(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;
+    char *filename = (argc > 5) ? argv[5]: 0;
+    if(0==strcmp(argv[2], "test")) test_dice(cfg, weights, filename);
+    else if(0==strcmp(argv[2], "train")) train_dice(cfg, weights);
+    else if(0==strcmp(argv[2], "valid")) validate_dice(cfg, weights);
+}
+

src/imagenet.c

@@ -8,6 +8,7 @@ void train_imagenet(char *cfgfile, char *weightfile)
     srand(time(0));
     float avg_loss = -1;
     char *base = basecfg(cfgfile);
+    char *backup_directory = "/home/pjreddie/backup/";
     printf("%s\n", base);
     network net = parse_network_cfg(cfgfile);
     if(weightfile){
@@ -50,7 +51,7 @@ void train_imagenet(char *cfgfile, char *weightfile)
         if((i % 30000) == 0) net.learning_rate *= .1;
         if(i%1000==0){
             char buff[256];
-            sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
+            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, i);
             save_weights(net, buff);
         }
     }