Split commands into different files

Makefile

@@ -25,7 +25,7 @@ CFLAGS+=-DGPU
 LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas
 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 normalization_layer.o parser.o option_list.o darknet.o detection_layer.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 normalization_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o detection.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
 endif

src/captcha.c

@@ -0,0 +1,120 @@
+#include "network.h"
+#include "utils.h"
+#include "parser.h"
+
+
+void train_captcha(char *cfgfile, char *weightfile)
+{
+    float avg_loss = -1;
+    srand(time(0));
+    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("/data/captcha/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_captcha(paths, imgs, plist->size, 10, 60, 200);
+        translate_data_rows(train, -128);
+        scale_data_rows(train, 1./128);
+        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){
+            char buff[256];
+            sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
+            save_weights(net, buff);
+        }
+    }
+}
+
+
+void validate_captcha(char *cfgfile, char *weightfile)
+{
+    srand(time(0));
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    int imgs = 1000;
+    int numchars = 37;
+    list *plist = get_paths("/data/captcha/valid.base");
+    char **paths = (char **)list_to_array(plist);
+    data valid = load_data_captcha(paths, imgs, 0, 10, 60, 200);
+    translate_data_rows(valid, -128);
+    scale_data_rows(valid, 1./128);
+    matrix pred = network_predict_data(net, valid);
+    int i, k;
+    int correct = 0;
+    int total = 0;
+    int accuracy = 0;
+    for(i = 0; i < imgs; ++i){
+        int allcorrect = 1;
+        for(k = 0; k < 10; ++k){
+            char truth = int_to_alphanum(max_index(valid.y.vals[i]+k*numchars, numchars));
+            char prediction = int_to_alphanum(max_index(pred.vals[i]+k*numchars, numchars));
+            if (truth != prediction) allcorrect=0;
+            if (truth != '.' && truth == prediction) ++correct;
+            if (truth != '.' || truth != prediction) ++total;
+        }
+        accuracy += allcorrect;
+    }
+    printf("Word Accuracy: %f, Char Accuracy %f\n", (float)accuracy/imgs, (float)correct/total);
+    free_data(valid);
+}
+
+void test_captcha(char *cfgfile, char *weightfile)
+{
+    setbuf(stdout, NULL);
+    srand(time(0));
+    //char *base = basecfg(cfgfile);
+    //printf("%s\n", base);
+    network net = parse_network_cfg(cfgfile);
+    set_batch_network(&net, 1);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    char filename[256];
+    while(1){
+        //printf("Enter filename: ");
+        fgets(filename, 256, stdin);
+        strtok(filename, "\n");
+        image im = load_image_color(filename, 60, 200);
+        translate_image(im, -128);
+        scale_image(im, 1/128.);
+        float *X = im.data;
+        float *predictions = network_predict(net, X);
+        print_letters(predictions, 10);
+        free_image(im);
+    }
+}
+void run_captcha(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], "test")) test_captcha(cfg, weights);
+    else if(0==strcmp(argv[2], "train")) train_captcha(cfg, weights);
+    else if(0==strcmp(argv[2], "valid")) validate_captcha(cfg, weights);
+}
+

src/darknet.c

@@ -1,246 +1,17 @@
-#include "connected_layer.h"
-#include "convolutional_layer.h"
-#include "maxpool_layer.h"
-#include "network.h"
-#include "image.h"
-#include "parser.h"
-#include "data.h"
-#include "matrix.h"
-#include "utils.h"
-#include "blas.h"
-#include "matrix.h"
-#include "server.h"
-
 #include <time.h>
 #include <stdlib.h>
 #include <stdio.h>
 
+#include "parser.h"
+#include "utils.h"
+#include "cuda.h"
+
 #define _GNU_SOURCE
 #include <fenv.h>
 
-void test_load()
+extern void run_imagenet(int argc, char **argv);
-{
+extern void run_detection(int argc, char **argv);
-    image dog = load_image("dog.jpg", 300, 400);
+extern void run_captcha(int argc, char **argv);
-    show_image(dog, "Test Load");
-    show_image_layers(dog, "Test Load");
-}
-
-void test_parser()
-{
-    network net = parse_network_cfg("cfg/trained_imagenet.cfg");
-    save_network(net, "cfg/trained_imagenet_smaller.cfg");
-}
-
-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)
-{
-    int classes = 20;
-    int elems = 4+classes;
-    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]);
-            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]);
-                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);
-            }
-        }
-    }
-    //printf("Done\n");
-    show_image(im, "box");
-    cvWaitKey(0);
-}
-
-char *basename(char *cfgfile)
-{
-    char *c = cfgfile;
-    char *next;
-    while((next = strchr(c, '/')))
-    {
-        c = next+1;
-    }
-    c = copy_string(c);
-    next = strchr(c, '_');
-    if (next) *next = 0;
-    next = strchr(c, '.');
-    if (next) *next = 0;
-    return c;
-}
-
-void train_detection_net(char *cfgfile, char *weightfile)
-{
-    char *base = basename(cfgfile);
-    printf("%s\n", base);
-    float avg_loss = 1;
-    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 = 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);
-    clock_t time;
-    while(1){
-        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);
-         */
-
-        printf("Loaded: %lf seconds\n", sec(clock()-time));
-        time=clock();
-        float loss = train_network(net, train);
-        net.seen += imgs;
-        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){
-            char buff[256];
-            sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
-            save_weights(net, buff);
-        }
-        free_data(train);
-    }
-}
-
-void validate_detection_net(char *cfgfile, char *weightfile)
-{
-    network net = parse_network_cfg(cfgfile);
-    if(weightfile){
-        load_weights(&net, weightfile);
-    }
-    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 **paths = (char **)list_to_array(plist);
-    int num_output = 1225;
-    int im_size = 448;
-    int classes = 21;
-
-    int m = plist->size;
-    int i = 0;
-    int splits = 100;
-    int num = (i+1)*m/splits - i*m/splits;
-
-    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;
-
-        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);
-    }
-}
-/*
-
-   void train_imagenet_distributed(char *address)
-   {
-   float avg_loss = 1;
-   srand(time(0));
-   network net = parse_network_cfg("cfg/net.cfg");
-   set_learning_network(&net, 0, 1, 0);
-   printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
-   int imgs = net.batch;
-   int i = 0;
-   char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
-   list *plist = get_paths("/data/imagenet/cls.train.list");
-   char **paths = (char **)list_to_array(plist);
-   printf("%d\n", plist->size);
-   clock_t time;
-   data train, buffer;
-   pthread_t load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
-   while(1){
-   i += 1;
-
-   time=clock();
-   client_update(net, address);
-   printf("Updated: %lf seconds\n", sec(clock()-time));
-
-   time=clock();
-   pthread_join(load_thread, 0);
-   train = buffer;
-   normalize_data_rows(train);
-   load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
-   printf("Loaded: %lf seconds\n", sec(clock()-time));
-   time=clock();
-
-   float loss = train_network(net, train);
-   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);
-   free_data(train);
-   }
-   }
- */
 
 void convert(char *cfgfile, char *outfile, char *weightfile)
 {
@@ -251,602 +22,6 @@ void convert(char *cfgfile, char *outfile, char *weightfile)
     save_network(net, outfile);
 }
 
-void train_captcha(char *cfgfile, char *weightfile)
-{
-    float avg_loss = -1;
-    srand(time(0));
-    char *base = basename(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("/data/captcha/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_captcha(paths, imgs, plist->size, 10, 60, 200);
-        translate_data_rows(train, -128);
-        scale_data_rows(train, 1./128);
-        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){
-            char buff[256];
-            sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
-            save_weights(net, buff);
-        }
-    }
-}
-
-
-void validate_captcha(char *cfgfile, char *weightfile)
-{
-    srand(time(0));
-    char *base = basename(cfgfile);
-    printf("%s\n", base);
-    network net = parse_network_cfg(cfgfile);
-    if(weightfile){
-        load_weights(&net, weightfile);
-    }
-    int imgs = 1000;
-    int numchars = 37;
-    list *plist = get_paths("/data/captcha/valid.base");
-    char **paths = (char **)list_to_array(plist);
-    data valid = load_data_captcha(paths, imgs, 0, 10, 60, 200);
-    translate_data_rows(valid, -128);
-    scale_data_rows(valid, 1./128);
-    matrix pred = network_predict_data(net, valid);
-    int i, k;
-    int correct = 0;
-    int total = 0;
-    int accuracy = 0;
-    for(i = 0; i < imgs; ++i){
-        int allcorrect = 1;
-        for(k = 0; k < 10; ++k){
-            char truth = int_to_alphanum(max_index(valid.y.vals[i]+k*numchars, numchars));
-            char prediction = int_to_alphanum(max_index(pred.vals[i]+k*numchars, numchars));
-            if (truth != prediction) allcorrect=0;
-            if (truth != '.' && truth == prediction) ++correct;
-            if (truth != '.' || truth != prediction) ++total;
-        }
-        accuracy += allcorrect;
-    }
-    printf("Word Accuracy: %f, Char Accuracy %f\n", (float)accuracy/imgs, (float)correct/total);
-    free_data(valid);
-}
-
-void test_captcha(char *cfgfile, char *weightfile)
-{
-    setbuf(stdout, NULL);
-    srand(time(0));
-    char *base = basename(cfgfile);
-    //printf("%s\n", base);
-    network net = parse_network_cfg(cfgfile);
-    set_batch_network(&net, 1);
-    if(weightfile){
-        load_weights(&net, weightfile);
-    }
-    clock_t time;
-    char filename[256];
-    while(1){
-        //printf("Enter filename: ");
-        fgets(filename, 256, stdin);
-        strtok(filename, "\n");
-        time = clock();
-        image im = load_image_color(filename, 60, 200);
-        translate_image(im, -128);
-        scale_image(im, 1/128.);
-        float *X = im.data;
-        time=clock();
-        float *predictions = network_predict(net, X);
-        //printf("Predicted in %f\n", sec(clock() - time));
-        print_letters(predictions, 10);
-        free_image(im);
-    }
-}
-
-void train_imagenet(char *cfgfile, char *weightfile)
-{
-    float avg_loss = -1;
-    srand(time(0));
-    char *base = basename(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;
-    char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
-    list *plist = get_paths("/data/imagenet/cls.train.list");
-    char **paths = (char **)list_to_array(plist);
-    printf("%d\n", plist->size);
-    clock_t time;
-    pthread_t load_thread;
-    data train;
-    data buffer;
-    load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
-    while(1){
-        ++i;
-        time=clock();
-        pthread_join(load_thread, 0);
-        train = buffer;
-        load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
-        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){
-            char buff[256];
-            sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
-            save_weights(net, buff);
-        }
-    }
-}
-
-void validate_imagenet(char *filename, char *weightfile)
-{
-    int i = 0;
-    network net = parse_network_cfg(filename);
-    if(weightfile){
-        load_weights(&net, weightfile);
-    }
-    srand(time(0));
-
-    char **labels = get_labels("/home/pjreddie/data/imagenet/cls.val.labels.list");
-
-    list *plist = get_paths("/data/imagenet/cls.val.list");
-    char **paths = (char **)list_to_array(plist);
-    int m = plist->size;
-    free_list(plist);
-
-    clock_t time;
-    float avg_acc = 0;
-    float avg_top5 = 0;
-    int splits = 50;
-    int num = (i+1)*m/splits - i*m/splits;
-
-    data val, buffer;
-    pthread_t load_thread = load_data_thread(paths, num, 0, labels, 1000, 256, 256, &buffer);
-    for(i = 1; i <= splits; ++i){
-        time=clock();
-
-        pthread_join(load_thread, 0);
-        val = buffer;
-
-        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, labels, 1000, 256, 256, &buffer);
-        printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
-
-        time=clock();
-        float *acc = network_accuracies(net, val);
-        avg_acc += acc[0];
-        avg_top5 += acc[1];
-        printf("%d: top1: %f, top5: %f, %lf seconds, %d images\n", i, avg_acc/i, avg_top5/i, sec(clock()-time), val.X.rows);
-        free_data(val);
-    }
-}
-
-void test_detection(char *cfgfile, char *weightfile)
-{
-    network net = parse_network_cfg(cfgfile);
-    if(weightfile){
-        load_weights(&net, weightfile);
-    }
-    int im_size = 448;
-    set_batch_network(&net, 1);
-    srand(2222222);
-    clock_t time;
-    char filename[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;
-        time=clock();
-        float *predictions = network_predict(net, X);
-        printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
-        draw_detection(im, predictions, 7);
-        free_image(im);
-    }
-}
-
-void test_init(char *cfgfile)
-{
-    gpu_index = -1;
-    network net = parse_network_cfg(cfgfile);
-    set_batch_network(&net, 1);
-    srand(2222222);
-    int i = 0;
-    char *filename = "data/test.jpg";
-
-    image im = load_image_color(filename, 256, 256);
-    //z_normalize_image(im);
-    translate_image(im, -128);
-    scale_image(im, 1/128.);
-    float *X = im.data;
-    forward_network(net, X, 0, 1);
-    for(i = 0; i < net.n; ++i){
-        if(net.types[i] == CONVOLUTIONAL){
-            convolutional_layer layer = *(convolutional_layer *)net.layers[i];
-            image output = get_convolutional_image(layer);
-            int size = output.h*output.w*output.c;
-            float v = variance_array(layer.output, size);
-            float m = mean_array(layer.output, size);
-            printf("%d: Convolutional, mean: %f, variance %f\n", i, m, v);
-        }
-        else if(net.types[i] == CONNECTED){
-            connected_layer layer = *(connected_layer *)net.layers[i];
-            int size = layer.outputs;
-            float v = variance_array(layer.output, size);
-            float m = mean_array(layer.output, size);
-            printf("%d: Connected, mean: %f, variance %f\n", i, m, v);
-        }
-    }
-    free_image(im);
-}
-void test_dog(char *cfgfile)
-{
-    image im = load_image_color("data/dog.jpg", 256, 256);
-    translate_image(im, -128);
-    print_image(im);
-    float *X = im.data;
-    network net = parse_network_cfg(cfgfile);
-    set_batch_network(&net, 1);
-    network_predict(net, X);
-    image crop = get_network_image_layer(net, 0);
-    show_image(crop, "cropped");
-    print_image(crop);
-    show_image(im, "orig");
-    float * inter = get_network_output(net);
-    pm(1000, 1, inter);
-    cvWaitKey(0);
-}
-
-void test_voc_segment(char *cfgfile, char *weightfile)
-{
-    network net = parse_network_cfg(cfgfile);
-    if(weightfile){
-        load_weights(&net, weightfile);
-    }
-    set_batch_network(&net, 1);
-    while(1){
-        char filename[256];
-        fgets(filename, 256, stdin);
-        strtok(filename, "\n");
-        image im = load_image_color(filename, 500, 500);
-        //resize_network(net, im.h, im.w, im.c);
-        translate_image(im, -128);
-        scale_image(im, 1/128.);
-        //float *predictions = network_predict(net, im.data);
-        network_predict(net, im.data);
-        free_image(im);
-        image output = get_network_image_layer(net, net.n-2);
-        show_image(output, "Segment Output");
-        cvWaitKey(0);
-    }
-}
-
-void test_imagenet(char *cfgfile)
-{
-    network net = parse_network_cfg(cfgfile);
-    set_batch_network(&net, 1);
-    //imgs=1;
-    srand(2222222);
-    int i = 0;
-    char **names = get_labels("cfg/shortnames.txt");
-    clock_t time;
-    char filename[256];
-    int indexes[10];
-    while(1){
-        fgets(filename, 256, stdin);
-        strtok(filename, "\n");
-        image im = load_image_color(filename, 256, 256);
-        translate_image(im, -128);
-        scale_image(im, 1/128.);
-        printf("%d %d %d\n", im.h, im.w, im.c);
-        float *X = im.data;
-        time=clock();
-        float *predictions = network_predict(net, X);
-        top_predictions(net, 10, indexes);
-        printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
-        for(i = 0; i < 10; ++i){
-            int index = indexes[i];
-            printf("%s: %f\n", names[index], predictions[index]);
-        }
-        free_image(im);
-    }
-}
-
-void test_visualize(char *filename)
-{
-    network net = parse_network_cfg(filename);
-    visualize_network(net);
-    cvWaitKey(0);
-}
-
-void test_cifar10(char *cfgfile)
-{
-    network net = parse_network_cfg(cfgfile);
-    data test = load_cifar10_data("data/cifar10/test_batch.bin");
-    clock_t start = clock(), end;
-    float test_acc = network_accuracy_multi(net, test, 10);
-    end = clock();
-    printf("%f in %f Sec\n", test_acc, sec(end-start));
-    //visualize_network(net);
-    //cvWaitKey(0);
-}
-
-void train_cifar10(char *cfgfile)
-{
-    srand(555555);
-    srand(time(0));
-    network net = parse_network_cfg(cfgfile);
-    data test = load_cifar10_data("data/cifar10/test_batch.bin");
-    int count = 0;
-    int iters = 50000/net.batch;
-    data train = load_all_cifar10();
-    while(++count <= 10000){
-        clock_t time = clock();
-        float loss = train_network_sgd(net, train, iters);
-
-        if(count%10 == 0){
-            float test_acc = network_accuracy(net, test);
-            printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,sec(clock()-time));
-            char buff[256];
-            sprintf(buff, "/home/pjreddie/imagenet_backup/cifar10_%d.cfg", count);
-            save_network(net, buff);
-        }else{
-            printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, sec(clock()-time));
-        }
-
-    }
-    free_data(train);
-}
-
-void compare_nist(char *p1,char *p2)
-{
-    srand(222222);
-    network n1 = parse_network_cfg(p1);
-    network n2 = parse_network_cfg(p2);
-    data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
-    normalize_data_rows(test);
-    compare_networks(n1, n2, test);
-}
-
-void test_nist(char *path)
-{
-    srand(222222);
-    network net = parse_network_cfg(path);
-    data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
-    normalize_data_rows(test);
-    clock_t start = clock(), end;
-    float test_acc = network_accuracy(net, test);
-    end = clock();
-    printf("Accuracy: %f, Time: %lf seconds\n", test_acc,(float)(end-start)/CLOCKS_PER_SEC);
-}
-
-void train_nist(char *cfgfile)
-{
-    srand(222222);
-    // srand(time(0));
-    data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
-    data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
-    network net = parse_network_cfg(cfgfile);
-    int count = 0;
-    int iters = 6000/net.batch + 1;
-    while(++count <= 100){
-        clock_t start = clock(), end;
-        normalize_data_rows(train);
-        normalize_data_rows(test);
-        float loss = train_network_sgd(net, train, iters);
-        float test_acc = 0;
-        if(count%1 == 0) test_acc = network_accuracy(net, test);
-        end = clock();
-        printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC);
-    }
-    free_data(train);
-    free_data(test);
-    char buff[256];
-    sprintf(buff, "%s.trained", cfgfile);
-    save_network(net, buff);
-}
-
-/*
-   void train_nist_distributed(char *address)
-   {
-   srand(time(0));
-   network net = parse_network_cfg("cfg/nist.client");
-   data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
-//data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
-normalize_data_rows(train);
-//normalize_data_rows(test);
-int count = 0;
-int iters = 50000/net.batch;
-iters = 1000/net.batch + 1;
-while(++count <= 2000){
-clock_t start = clock(), end;
-float loss = train_network_sgd(net, train, iters);
-client_update(net, address);
-end = clock();
-//float test_acc = network_accuracy_gpu(net, test);
-//float test_acc = 0;
-printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC);
-}
-}
- */
-
-void test_ensemble()
-{
-    int i;
-    srand(888888);
-    data d = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
-    normalize_data_rows(d);
-    data test = load_categorical_data_csv("mnist/mnist_test.csv", 0,10);
-    normalize_data_rows(test);
-    data train = d;
-    //   data *split = split_data(d, 1, 10);
-    //   data train = split[0];
-    //   data test = split[1];
-    matrix prediction = make_matrix(test.y.rows, test.y.cols);
-    int n = 30;
-    for(i = 0; i < n; ++i){
-        int count = 0;
-        float lr = .0005;
-        float momentum = .9;
-        float decay = .01;
-        network net = parse_network_cfg("nist.cfg");
-        while(++count <= 15){
-            float acc = train_network_sgd(net, train, train.X.rows);
-            printf("Training Accuracy: %lf Learning Rate: %f Momentum: %f Decay: %f\n", acc, lr, momentum, decay );
-            lr /= 2; 
-        }
-        matrix partial = network_predict_data(net, test);
-        float acc = matrix_topk_accuracy(test.y, partial,1);
-        printf("Model Accuracy: %lf\n", acc);
-        matrix_add_matrix(partial, prediction);
-        acc = matrix_topk_accuracy(test.y, prediction,1);
-        printf("Current Ensemble Accuracy: %lf\n", acc);
-        free_matrix(partial);
-    }
-    float acc = matrix_topk_accuracy(test.y, prediction,1);
-    printf("Full Ensemble Accuracy: %lf\n", acc);
-}
-
-void visualize_cat()
-{
-    network net = parse_network_cfg("cfg/voc_imagenet.cfg");
-    image im = load_image_color("data/cat.png", 0, 0);
-    printf("Processing %dx%d image\n", im.h, im.w);
-    resize_network(net, im.h, im.w, im.c);
-    forward_network(net, im.data, 0, 0);
-
-    visualize_network(net);
-    cvWaitKey(0);
-}
-
-void test_correct_nist()
-{
-    network net = parse_network_cfg("cfg/nist_conv.cfg");
-    srand(222222);
-    net = parse_network_cfg("cfg/nist_conv.cfg");
-    data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
-    data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
-    normalize_data_rows(train);
-    normalize_data_rows(test);
-    int count = 0;
-    int iters = 1000/net.batch;
-
-    while(++count <= 5){
-        clock_t start = clock(), end;
-        float loss = train_network_sgd(net, train, iters);
-        end = clock();
-        float test_acc = network_accuracy(net, test);
-        printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
-    }
-    save_network(net, "cfg/nist_gpu.cfg");
-
-    gpu_index = -1;
-    count = 0;
-    srand(222222);
-    net = parse_network_cfg("cfg/nist_conv.cfg");
-    while(++count <= 5){
-        clock_t start = clock(), end;
-        float loss = train_network_sgd(net, train, iters);
-        end = clock();
-        float test_acc = network_accuracy(net, test);
-        printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
-    }
-    save_network(net, "cfg/nist_cpu.cfg");
-}
-
-void test_correct_alexnet()
-{
-    char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
-    list *plist = get_paths("/data/imagenet/cls.train.list");
-    char **paths = (char **)list_to_array(plist);
-    printf("%d\n", plist->size);
-    clock_t time;
-    int count = 0;
-    network net;
-
-    srand(222222);
-    net = parse_network_cfg("cfg/net.cfg");
-    int imgs = net.batch;
-
-    count = 0;
-    while(++count <= 5){
-        time=clock();
-        data train = load_data(paths, imgs, plist->size, labels, 1000, 256, 256);
-        normalize_data_rows(train);
-        printf("Loaded: %lf seconds\n", sec(clock()-time));
-        time=clock();
-        float loss = train_network(net, train);
-        printf("%d: %f, %lf seconds, %d images\n", count, loss, sec(clock()-time), imgs*net.batch);
-        free_data(train);
-    }
-
-    gpu_index = -1;
-    count = 0;
-    srand(222222);
-    net = parse_network_cfg("cfg/net.cfg");
-    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
-    while(++count <= 5){
-        time=clock();
-        data train = load_data(paths, imgs, plist->size, labels, 1000, 256,256);
-        normalize_data_rows(train);
-        printf("Loaded: %lf seconds\n", sec(clock()-time));
-        time=clock();
-        float loss = train_network(net, train);
-        printf("%d: %f, %lf seconds, %d images\n", count, loss, sec(clock()-time), imgs*net.batch);
-        free_data(train);
-    }
-}
-
-/*
-   void run_server()
-   {
-   srand(time(0));
-   network net = parse_network_cfg("cfg/net.cfg");
-   set_batch_network(&net, 1);
-   server_update(net);
-   }
-
-   void test_client()
-   {
-   network net = parse_network_cfg("cfg/alexnet.client");
-   clock_t time=clock();
-   client_update(net, "localhost");
-   printf("1\n");
-   client_update(net, "localhost");
-   printf("2\n");
-   client_update(net, "localhost");
-   printf("3\n");
-   printf("Transfered: %lf seconds\n", sec(clock()-time));
-   }
- */
-
 void del_arg(int argc, char **argv, int index)
 {
     int i;
@@ -914,44 +89,13 @@ int main(int argc, char **argv)
     }
 #endif
 
-    if(0==strcmp(argv[1], "test_correct")) test_correct_alexnet();
+    if(0==strcmp(argv[1], "imagenet")){
-    else if(0==strcmp(argv[1], "test_correct_nist")) test_correct_nist();
+        run_imagenet(argc, argv);   
-    //else if(0==strcmp(argv[1], "server")) run_server();
+    } else if (0 == strcmp(argv[1], "detection")){
-
+        run_detection(argc, argv);   
-#ifdef GPU
+    } else if (0 == strcmp(argv[1], "captcha")){
-    else if(0==strcmp(argv[1], "test_gpu")) test_gpu_blas();
+        run_captcha(argc, argv);   
-#endif
-
-    else if(argc < 3){
-        fprintf(stderr, "usage: %s <function> <filename>\n", argv[0]);
-        return 0;
     }
-    else if(0==strcmp(argv[1], "detection")) train_detection_net(argv[2], (argc > 3)? argv[3] : 0);
-    else if(0==strcmp(argv[1], "test")) test_imagenet(argv[2]);
-    else if(0==strcmp(argv[1], "dog")) test_dog(argv[2]);
-    else if(0==strcmp(argv[1], "ctrain")) train_cifar10(argv[2]);
-    else if(0==strcmp(argv[1], "nist")) train_nist(argv[2]);
-    else if(0==strcmp(argv[1], "ctest")) test_cifar10(argv[2]);
-    else if(0==strcmp(argv[1], "train")) train_imagenet(argv[2], (argc > 3)? argv[3] : 0);
-    else if(0==strcmp(argv[1], "captcha")) train_captcha(argv[2], (argc > 3)? argv[3] : 0);
-    else if(0==strcmp(argv[1], "tcaptcha")) test_captcha(argv[2], (argc > 3)? argv[3] : 0);
-    else if(0==strcmp(argv[1], "vcaptcha")) validate_captcha(argv[2], (argc > 3)? argv[3] : 0);
-    else if(0==strcmp(argv[1], "testseg")) test_voc_segment(argv[2], (argc > 3)? argv[3] : 0);
-    //else if(0==strcmp(argv[1], "client")) train_imagenet_distributed(argv[2]);
-    else if(0==strcmp(argv[1], "detect")) test_detection(argv[2], (argc > 3)? argv[3] : 0);
-    else if(0==strcmp(argv[1], "init")) test_init(argv[2]);
-    else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
-    else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2], (argc > 3)? argv[3] : 0);
-    else if(0==strcmp(argv[1], "testnist")) test_nist(argv[2]);
-    else if(0==strcmp(argv[1], "validetect")) validate_detection_net(argv[2], (argc > 3)? argv[3] : 0);
-    else if(argc < 4){
-        fprintf(stderr, "usage: %s <function> <filename> <filename>\n", argv[0]);
-        return 0;
-    }
-    else if(0==strcmp(argv[1], "compare")) compare_nist(argv[2], argv[3]);
-    else if(0==strcmp(argv[1], "convert")) convert(argv[2], argv[3], (argc > 4)? argv[4] : 0);
-    else if(0==strcmp(argv[1], "scale")) scale_rate(argv[2], atof(argv[3]));
-    fprintf(stderr, "Success!\n");
     return 0;
 }
 

src/detection.c

@@ -0,0 +1,200 @@
+#include "network.h"
+#include "utils.h"
+#include "parser.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)
+{
+    int classes = 20;
+    int elems = 4+classes;
+    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]);
+            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]);
+                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);
+            }
+        }
+    }
+    //printf("Done\n");
+    show_image(im, "box");
+    cvWaitKey(0);
+}
+
+void train_detection(char *cfgfile, char *weightfile)
+{
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    float avg_loss = 1;
+    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 = 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);
+    clock_t time;
+    while(1){
+        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);
+         */
+
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+        time=clock();
+        float loss = train_network(net, train);
+        net.seen += imgs;
+        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){
+            char buff[256];
+            sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
+            save_weights(net, buff);
+        }
+        free_data(train);
+    }
+}
+
+void validate_detection(char *cfgfile, char *weightfile)
+{
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    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 **paths = (char **)list_to_array(plist);
+    int num_output = 1225;
+    int im_size = 448;
+    int classes = 21;
+
+    int m = plist->size;
+    int i = 0;
+    int splits = 100;
+    int num = (i+1)*m/splits - i*m/splits;
+
+    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;
+
+        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);
+    }
+}
+
+void test_detection(char *cfgfile, char *weightfile)
+{
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    int im_size = 448;
+    set_batch_network(&net, 1);
+    srand(2222222);
+    clock_t time;
+    char filename[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;
+        time=clock();
+        float *predictions = network_predict(net, X);
+        printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
+        draw_detection(im, predictions, 7);
+        free_image(im);
+    }
+}
+
+void run_detection(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], "test")) test_detection(cfg, weights);
+    else if(0==strcmp(argv[2], "train")) train_detection(cfg, weights);
+    else if(0==strcmp(argv[2], "valid")) validate_detection(cfg, weights);
+}

src/imagenet.c

@@ -0,0 +1,180 @@
+#include "network.h"
+#include "utils.h"
+#include "parser.h"
+
+void train_imagenet(char *cfgfile, char *weightfile)
+{
+    float avg_loss = -1;
+    srand(time(0));
+    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;
+    char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
+    list *plist = get_paths("/data/imagenet/cls.train.list");
+    char **paths = (char **)list_to_array(plist);
+    printf("%d\n", plist->size);
+    clock_t time;
+    pthread_t load_thread;
+    data train;
+    data buffer;
+    load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
+    while(1){
+        ++i;
+        time=clock();
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
+        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){
+            char buff[256];
+            sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
+            save_weights(net, buff);
+        }
+    }
+}
+
+void validate_imagenet(char *filename, char *weightfile)
+{
+    int i = 0;
+    network net = parse_network_cfg(filename);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    srand(time(0));
+
+    char **labels = get_labels("/home/pjreddie/data/imagenet/cls.val.labels.list");
+
+    list *plist = get_paths("/data/imagenet/cls.val.list");
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    clock_t time;
+    float avg_acc = 0;
+    float avg_top5 = 0;
+    int splits = 50;
+    int num = (i+1)*m/splits - i*m/splits;
+
+    data val, buffer;
+    pthread_t load_thread = load_data_thread(paths, num, 0, labels, 1000, 256, 256, &buffer);
+    for(i = 1; i <= splits; ++i){
+        time=clock();
+
+        pthread_join(load_thread, 0);
+        val = buffer;
+
+        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, labels, 1000, 256, 256, &buffer);
+        printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
+
+        time=clock();
+        float *acc = network_accuracies(net, val);
+        avg_acc += acc[0];
+        avg_top5 += acc[1];
+        printf("%d: top1: %f, top5: %f, %lf seconds, %d images\n", i, avg_acc/i, avg_top5/i, sec(clock()-time), val.X.rows);
+        free_data(val);
+    }
+}
+
+void test_imagenet(char *cfgfile, char *weightfile)
+{
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    set_batch_network(&net, 1);
+    //imgs=1;
+    srand(2222222);
+    int i = 0;
+    char **names = get_labels("cfg/shortnames.txt");
+    clock_t time;
+    char filename[256];
+    int indexes[10];
+    while(1){
+        fgets(filename, 256, stdin);
+        strtok(filename, "\n");
+        image im = load_image_color(filename, 256, 256);
+        translate_image(im, -128);
+        scale_image(im, 1/128.);
+        printf("%d %d %d\n", im.h, im.w, im.c);
+        float *X = im.data;
+        time=clock();
+        float *predictions = network_predict(net, X);
+        top_predictions(net, 10, indexes);
+        printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
+        for(i = 0; i < 10; ++i){
+            int index = indexes[i];
+            printf("%s: %f\n", names[index], predictions[index]);
+        }
+        free_image(im);
+    }
+}
+
+void run_imagenet(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], "test")) test_imagenet(cfg, weights);
+    else if(0==strcmp(argv[2], "train")) train_imagenet(cfg, weights);
+    else if(0==strcmp(argv[2], "valid")) validate_imagenet(cfg, weights);
+}
+
+/*
+   void train_imagenet_distributed(char *address)
+   {
+   float avg_loss = 1;
+   srand(time(0));
+   network net = parse_network_cfg("cfg/net.cfg");
+   set_learning_network(&net, 0, 1, 0);
+   printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+   int imgs = net.batch;
+   int i = 0;
+   char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
+   list *plist = get_paths("/data/imagenet/cls.train.list");
+   char **paths = (char **)list_to_array(plist);
+   printf("%d\n", plist->size);
+   clock_t time;
+   data train, buffer;
+   pthread_t load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
+   while(1){
+   i += 1;
+
+   time=clock();
+   client_update(net, address);
+   printf("Updated: %lf seconds\n", sec(clock()-time));
+
+   time=clock();
+   pthread_join(load_thread, 0);
+   train = buffer;
+   normalize_data_rows(train);
+   load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 224, 224, &buffer);
+   printf("Loaded: %lf seconds\n", sec(clock()-time));
+   time=clock();
+
+   float loss = train_network(net, train);
+   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);
+   free_data(train);
+   }
+   }
+ */
+

src/old.c

@@ -0,0 +1,356 @@
+
+void test_load()
+{
+    image dog = load_image("dog.jpg", 300, 400);
+    show_image(dog, "Test Load");
+    show_image_layers(dog, "Test Load");
+}
+
+void test_parser()
+{
+    network net = parse_network_cfg("cfg/trained_imagenet.cfg");
+    save_network(net, "cfg/trained_imagenet_smaller.cfg");
+}
+
+void test_init(char *cfgfile)
+{
+    gpu_index = -1;
+    network net = parse_network_cfg(cfgfile);
+    set_batch_network(&net, 1);
+    srand(2222222);
+    int i = 0;
+    char *filename = "data/test.jpg";
+
+    image im = load_image_color(filename, 256, 256);
+    //z_normalize_image(im);
+    translate_image(im, -128);
+    scale_image(im, 1/128.);
+    float *X = im.data;
+    forward_network(net, X, 0, 1);
+    for(i = 0; i < net.n; ++i){
+        if(net.types[i] == CONVOLUTIONAL){
+            convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+            image output = get_convolutional_image(layer);
+            int size = output.h*output.w*output.c;
+            float v = variance_array(layer.output, size);
+            float m = mean_array(layer.output, size);
+            printf("%d: Convolutional, mean: %f, variance %f\n", i, m, v);
+        }
+        else if(net.types[i] == CONNECTED){
+            connected_layer layer = *(connected_layer *)net.layers[i];
+            int size = layer.outputs;
+            float v = variance_array(layer.output, size);
+            float m = mean_array(layer.output, size);
+            printf("%d: Connected, mean: %f, variance %f\n", i, m, v);
+        }
+    }
+    free_image(im);
+}
+void test_dog(char *cfgfile)
+{
+    image im = load_image_color("data/dog.jpg", 256, 256);
+    translate_image(im, -128);
+    print_image(im);
+    float *X = im.data;
+    network net = parse_network_cfg(cfgfile);
+    set_batch_network(&net, 1);
+    network_predict(net, X);
+    image crop = get_network_image_layer(net, 0);
+    show_image(crop, "cropped");
+    print_image(crop);
+    show_image(im, "orig");
+    float * inter = get_network_output(net);
+    pm(1000, 1, inter);
+    cvWaitKey(0);
+}
+
+void test_voc_segment(char *cfgfile, char *weightfile)
+{
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    set_batch_network(&net, 1);
+    while(1){
+        char filename[256];
+        fgets(filename, 256, stdin);
+        strtok(filename, "\n");
+        image im = load_image_color(filename, 500, 500);
+        //resize_network(net, im.h, im.w, im.c);
+        translate_image(im, -128);
+        scale_image(im, 1/128.);
+        //float *predictions = network_predict(net, im.data);
+        network_predict(net, im.data);
+        free_image(im);
+        image output = get_network_image_layer(net, net.n-2);
+        show_image(output, "Segment Output");
+        cvWaitKey(0);
+    }
+}
+void test_visualize(char *filename)
+{
+    network net = parse_network_cfg(filename);
+    visualize_network(net);
+    cvWaitKey(0);
+}
+
+void test_cifar10(char *cfgfile)
+{
+    network net = parse_network_cfg(cfgfile);
+    data test = load_cifar10_data("data/cifar10/test_batch.bin");
+    clock_t start = clock(), end;
+    float test_acc = network_accuracy_multi(net, test, 10);
+    end = clock();
+    printf("%f in %f Sec\n", test_acc, sec(end-start));
+    //visualize_network(net);
+    //cvWaitKey(0);
+}
+
+void train_cifar10(char *cfgfile)
+{
+    srand(555555);
+    srand(time(0));
+    network net = parse_network_cfg(cfgfile);
+    data test = load_cifar10_data("data/cifar10/test_batch.bin");
+    int count = 0;
+    int iters = 50000/net.batch;
+    data train = load_all_cifar10();
+    while(++count <= 10000){
+        clock_t time = clock();
+        float loss = train_network_sgd(net, train, iters);
+
+        if(count%10 == 0){
+            float test_acc = network_accuracy(net, test);
+            printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,sec(clock()-time));
+            char buff[256];
+            sprintf(buff, "/home/pjreddie/imagenet_backup/cifar10_%d.cfg", count);
+            save_network(net, buff);
+        }else{
+            printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, sec(clock()-time));
+        }
+
+    }
+    free_data(train);
+}
+
+void compare_nist(char *p1,char *p2)
+{
+    srand(222222);
+    network n1 = parse_network_cfg(p1);
+    network n2 = parse_network_cfg(p2);
+    data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
+    normalize_data_rows(test);
+    compare_networks(n1, n2, test);
+}
+
+void test_nist(char *path)
+{
+    srand(222222);
+    network net = parse_network_cfg(path);
+    data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
+    normalize_data_rows(test);
+    clock_t start = clock(), end;
+    float test_acc = network_accuracy(net, test);
+    end = clock();
+    printf("Accuracy: %f, Time: %lf seconds\n", test_acc,(float)(end-start)/CLOCKS_PER_SEC);
+}
+
+void train_nist(char *cfgfile)
+{
+    srand(222222);
+    // srand(time(0));
+    data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
+    data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
+    network net = parse_network_cfg(cfgfile);
+    int count = 0;
+    int iters = 6000/net.batch + 1;
+    while(++count <= 100){
+        clock_t start = clock(), end;
+        normalize_data_rows(train);
+        normalize_data_rows(test);
+        float loss = train_network_sgd(net, train, iters);
+        float test_acc = 0;
+        if(count%1 == 0) test_acc = network_accuracy(net, test);
+        end = clock();
+        printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC);
+    }
+    free_data(train);
+    free_data(test);
+    char buff[256];
+    sprintf(buff, "%s.trained", cfgfile);
+    save_network(net, buff);
+}
+
+/*
+   void train_nist_distributed(char *address)
+   {
+   srand(time(0));
+   network net = parse_network_cfg("cfg/nist.client");
+   data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
+//data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
+normalize_data_rows(train);
+//normalize_data_rows(test);
+int count = 0;
+int iters = 50000/net.batch;
+iters = 1000/net.batch + 1;
+while(++count <= 2000){
+clock_t start = clock(), end;
+float loss = train_network_sgd(net, train, iters);
+client_update(net, address);
+end = clock();
+//float test_acc = network_accuracy_gpu(net, test);
+//float test_acc = 0;
+printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, (float)(end-start)/CLOCKS_PER_SEC);
+}
+}
+ */
+
+void test_ensemble()
+{
+    int i;
+    srand(888888);
+    data d = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
+    normalize_data_rows(d);
+    data test = load_categorical_data_csv("mnist/mnist_test.csv", 0,10);
+    normalize_data_rows(test);
+    data train = d;
+    //   data *split = split_data(d, 1, 10);
+    //   data train = split[0];
+    //   data test = split[1];
+    matrix prediction = make_matrix(test.y.rows, test.y.cols);
+    int n = 30;
+    for(i = 0; i < n; ++i){
+        int count = 0;
+        float lr = .0005;
+        float momentum = .9;
+        float decay = .01;
+        network net = parse_network_cfg("nist.cfg");
+        while(++count <= 15){
+            float acc = train_network_sgd(net, train, train.X.rows);
+            printf("Training Accuracy: %lf Learning Rate: %f Momentum: %f Decay: %f\n", acc, lr, momentum, decay );
+            lr /= 2; 
+        }
+        matrix partial = network_predict_data(net, test);
+        float acc = matrix_topk_accuracy(test.y, partial,1);
+        printf("Model Accuracy: %lf\n", acc);
+        matrix_add_matrix(partial, prediction);
+        acc = matrix_topk_accuracy(test.y, prediction,1);
+        printf("Current Ensemble Accuracy: %lf\n", acc);
+        free_matrix(partial);
+    }
+    float acc = matrix_topk_accuracy(test.y, prediction,1);
+    printf("Full Ensemble Accuracy: %lf\n", acc);
+}
+
+void visualize_cat()
+{
+    network net = parse_network_cfg("cfg/voc_imagenet.cfg");
+    image im = load_image_color("data/cat.png", 0, 0);
+    printf("Processing %dx%d image\n", im.h, im.w);
+    resize_network(net, im.h, im.w, im.c);
+    forward_network(net, im.data, 0, 0);
+
+    visualize_network(net);
+    cvWaitKey(0);
+}
+
+void test_correct_nist()
+{
+    network net = parse_network_cfg("cfg/nist_conv.cfg");
+    srand(222222);
+    net = parse_network_cfg("cfg/nist_conv.cfg");
+    data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
+    data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
+    normalize_data_rows(train);
+    normalize_data_rows(test);
+    int count = 0;
+    int iters = 1000/net.batch;
+
+    while(++count <= 5){
+        clock_t start = clock(), end;
+        float loss = train_network_sgd(net, train, iters);
+        end = clock();
+        float test_acc = network_accuracy(net, test);
+        printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
+    }
+    save_network(net, "cfg/nist_gpu.cfg");
+
+    gpu_index = -1;
+    count = 0;
+    srand(222222);
+    net = parse_network_cfg("cfg/nist_conv.cfg");
+    while(++count <= 5){
+        clock_t start = clock(), end;
+        float loss = train_network_sgd(net, train, iters);
+        end = clock();
+        float test_acc = network_accuracy(net, test);
+        printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
+    }
+    save_network(net, "cfg/nist_cpu.cfg");
+}
+
+void test_correct_alexnet()
+{
+    char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
+    list *plist = get_paths("/data/imagenet/cls.train.list");
+    char **paths = (char **)list_to_array(plist);
+    printf("%d\n", plist->size);
+    clock_t time;
+    int count = 0;
+    network net;
+
+    srand(222222);
+    net = parse_network_cfg("cfg/net.cfg");
+    int imgs = net.batch;
+
+    count = 0;
+    while(++count <= 5){
+        time=clock();
+        data train = load_data(paths, imgs, plist->size, labels, 1000, 256, 256);
+        normalize_data_rows(train);
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+        time=clock();
+        float loss = train_network(net, train);
+        printf("%d: %f, %lf seconds, %d images\n", count, loss, sec(clock()-time), imgs*net.batch);
+        free_data(train);
+    }
+
+    gpu_index = -1;
+    count = 0;
+    srand(222222);
+    net = parse_network_cfg("cfg/net.cfg");
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+    while(++count <= 5){
+        time=clock();
+        data train = load_data(paths, imgs, plist->size, labels, 1000, 256,256);
+        normalize_data_rows(train);
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+        time=clock();
+        float loss = train_network(net, train);
+        printf("%d: %f, %lf seconds, %d images\n", count, loss, sec(clock()-time), imgs*net.batch);
+        free_data(train);
+    }
+}
+
+/*
+   void run_server()
+   {
+   srand(time(0));
+   network net = parse_network_cfg("cfg/net.cfg");
+   set_batch_network(&net, 1);
+   server_update(net);
+   }
+
+   void test_client()
+   {
+   network net = parse_network_cfg("cfg/alexnet.client");
+   clock_t time=clock();
+   client_update(net, "localhost");
+   printf("1\n");
+   client_update(net, "localhost");
+   printf("2\n");
+   client_update(net, "localhost");
+   printf("3\n");
+   printf("Transfered: %lf seconds\n", sec(clock()-time));
+   }
+ */

src/utils.c

@@ -9,6 +9,22 @@
 #include "utils.h"
 
 
+char *basecfg(char *cfgfile)
+{
+    char *c = cfgfile;
+    char *next;
+    while((next = strchr(c, '/')))
+    {
+        c = next+1;
+    }
+    c = copy_string(c);
+    next = strchr(c, '_');
+    if (next) *next = 0;
+    next = strchr(c, '.');
+    if (next) *next = 0;
+    return c;
+}
+
 int alphanum_to_int(char c)
 {
     return (c < 58) ? c - 48 : c-87;

src/utils.h

@@ -4,6 +4,7 @@
 #include <time.h>
 #include "list.h"
 
+char *basecfg(char *cfgfile);
 int alphanum_to_int(char c);
 char int_to_alphanum(int i);
 void read_all(int fd, char *buffer, size_t bytes);