From 8a504c737d2bc3b9b37cb79cb50fbf7eecda07df Mon Sep 17 00:00:00 2001
From: Tino Hager <tino.hager@nager.at>
Date: Wed, 27 Jun 2018 21:56:47 +0000
Subject: [PATCH] repair tabs spaces
---
src/darknet.c | 1327 ++++++++++++++++++---------------------------------------
1 files changed, 416 insertions(+), 911 deletions(-)
diff --git a/src/darknet.c b/src/darknet.c
index 413d7f2..f6d61c0 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -1,956 +1,461 @@
-#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>
-#define _GNU_SOURCE
-#include <fenv.h>
+#include "parser.h"
+#include "utils.h"
+#include "cuda.h"
+#include "blas.h"
+#include "connected_layer.h"
-void test_load()
+#ifdef OPENCV
+#include "opencv2/highgui/highgui_c.h"
+#endif
+
+extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top);
+extern void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int ext_output);
+extern void run_voxel(int argc, char **argv);
+extern void run_yolo(int argc, char **argv);
+extern void run_detector(int argc, char **argv);
+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);
+extern void run_compare(int argc, char **argv);
+extern void run_classifier(int argc, char **argv);
+extern void run_char_rnn(int argc, char **argv);
+extern void run_vid_rnn(int argc, char **argv);
+extern void run_tag(int argc, char **argv);
+extern void run_cifar(int argc, char **argv);
+extern void run_go(int argc, char **argv);
+extern void run_art(int argc, char **argv);
+extern void run_super(int argc, char **argv);
+
+void average(int argc, char *argv[])
{
- 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");
-}
-
-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)
-{
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, 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.list");
- 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)
-{
- 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)
-{
+ char *cfgfile = argv[2];
+ char *outfile = argv[3];
gpu_index = -1;
network net = parse_network_cfg(cfgfile);
- set_batch_network(&net, 1);
- srand(2222222);
- int i = 0;
- char *filename = "data/test.jpg";
+ network sum = parse_network_cfg(cfgfile);
- 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);
-}
+ char *weightfile = argv[4];
+ load_weights(&sum, weightfile);
-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;
+ int i, j;
+ int n = argc - 5;
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;
- for(i = index; i < argc-1; ++i) argv[i] = argv[i+1];
- argv[i] = 0;
-}
-
-int find_arg(int argc, char* argv[], char *arg)
-{
- int i;
- for(i = 0; i < argc; ++i) {
- if(!argv[i]) continue;
- if(0==strcmp(argv[i], arg)) {
- del_arg(argc, argv, i);
- return 1;
+ weightfile = argv[i+5];
+ load_weights(&net, weightfile);
+ for(j = 0; j < net.n; ++j){
+ layer l = net.layers[j];
+ layer out = sum.layers[j];
+ if(l.type == CONVOLUTIONAL){
+ int num = l.n*l.c*l.size*l.size;
+ axpy_cpu(l.n, 1, l.biases, 1, out.biases, 1);
+ axpy_cpu(num, 1, l.weights, 1, out.weights, 1);
+ if(l.batch_normalize){
+ axpy_cpu(l.n, 1, l.scales, 1, out.scales, 1);
+ axpy_cpu(l.n, 1, l.rolling_mean, 1, out.rolling_mean, 1);
+ axpy_cpu(l.n, 1, l.rolling_variance, 1, out.rolling_variance, 1);
+ }
+ }
+ if(l.type == CONNECTED){
+ axpy_cpu(l.outputs, 1, l.biases, 1, out.biases, 1);
+ axpy_cpu(l.outputs*l.inputs, 1, l.weights, 1, out.weights, 1);
+ }
}
}
- return 0;
+ n = n+1;
+ for(j = 0; j < net.n; ++j){
+ layer l = sum.layers[j];
+ if(l.type == CONVOLUTIONAL){
+ int num = l.n*l.c*l.size*l.size;
+ scal_cpu(l.n, 1./n, l.biases, 1);
+ scal_cpu(num, 1./n, l.weights, 1);
+ if(l.batch_normalize){
+ scal_cpu(l.n, 1./n, l.scales, 1);
+ scal_cpu(l.n, 1./n, l.rolling_mean, 1);
+ scal_cpu(l.n, 1./n, l.rolling_variance, 1);
+ }
+ }
+ if(l.type == CONNECTED){
+ scal_cpu(l.outputs, 1./n, l.biases, 1);
+ scal_cpu(l.outputs*l.inputs, 1./n, l.weights, 1);
+ }
+ }
+ save_weights(sum, outfile);
}
-int find_int_arg(int argc, char **argv, char *arg, int def)
+void speed(char *cfgfile, int tics)
{
+ if (tics == 0) tics = 1000;
+ network net = parse_network_cfg(cfgfile);
+ set_batch_network(&net, 1);
int i;
- for(i = 0; i < argc-1; ++i){
- if(!argv[i]) continue;
- if(0==strcmp(argv[i], arg)){
- def = atoi(argv[i+1]);
- del_arg(argc, argv, i);
- del_arg(argc, argv, i);
+ time_t start = time(0);
+ image im = make_image(net.w, net.h, net.c);
+ for(i = 0; i < tics; ++i){
+ network_predict(net, im.data);
+ }
+ double t = difftime(time(0), start);
+ printf("\n%d evals, %f Seconds\n", tics, t);
+ printf("Speed: %f sec/eval\n", t/tics);
+ printf("Speed: %f Hz\n", tics/t);
+}
+
+void operations(char *cfgfile)
+{
+ gpu_index = -1;
+ network net = parse_network_cfg(cfgfile);
+ int i;
+ long ops = 0;
+ for(i = 0; i < net.n; ++i){
+ layer l = net.layers[i];
+ if(l.type == CONVOLUTIONAL){
+ ops += 2l * l.n * l.size*l.size*l.c * l.out_h*l.out_w;
+ } else if(l.type == CONNECTED){
+ ops += 2l * l.inputs * l.outputs;
+ }
+ }
+ printf("Floating Point Operations: %ld\n", ops);
+ printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.);
+}
+
+void oneoff(char *cfgfile, char *weightfile, char *outfile)
+{
+ gpu_index = -1;
+ network net = parse_network_cfg(cfgfile);
+ int oldn = net.layers[net.n - 2].n;
+ int c = net.layers[net.n - 2].c;
+ net.layers[net.n - 2].n = 9372;
+ net.layers[net.n - 2].biases += 5;
+ net.layers[net.n - 2].weights += 5*c;
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ net.layers[net.n - 2].biases -= 5;
+ net.layers[net.n - 2].weights -= 5*c;
+ net.layers[net.n - 2].n = oldn;
+ printf("%d\n", oldn);
+ layer l = net.layers[net.n - 2];
+ copy_cpu(l.n/3, l.biases, 1, l.biases + l.n/3, 1);
+ copy_cpu(l.n/3, l.biases, 1, l.biases + 2*l.n/3, 1);
+ copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + l.n/3*l.c, 1);
+ copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + 2*l.n/3*l.c, 1);
+ *net.seen = 0;
+ save_weights(net, outfile);
+}
+
+void partial(char *cfgfile, char *weightfile, char *outfile, int max)
+{
+ gpu_index = -1;
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights_upto(&net, weightfile, max);
+ }
+ *net.seen = 0;
+ save_weights_upto(net, outfile, max);
+}
+
+#include "convolutional_layer.h"
+void rescale_net(char *cfgfile, char *weightfile, char *outfile)
+{
+ gpu_index = -1;
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ int i;
+ for(i = 0; i < net.n; ++i){
+ layer l = net.layers[i];
+ if(l.type == CONVOLUTIONAL){
+ rescale_weights(l, 2, -.5);
break;
}
}
- return def;
+ save_weights(net, outfile);
}
-void scale_rate(char *filename, float scale)
+void rgbgr_net(char *cfgfile, char *weightfile, char *outfile)
{
- // Ready for some weird shit??
- FILE *fp = fopen(filename, "r+b");
- if(!fp) file_error(filename);
- float rate = 0;
- fread(&rate, sizeof(float), 1, fp);
- printf("Scaling learning rate from %f to %f\n", rate, rate*scale);
- rate = rate*scale;
- fseek(fp, 0, SEEK_SET);
- fwrite(&rate, sizeof(float), 1, fp);
- fclose(fp);
+ gpu_index = -1;
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ int i;
+ for(i = 0; i < net.n; ++i){
+ layer l = net.layers[i];
+ if(l.type == CONVOLUTIONAL){
+ rgbgr_weights(l);
+ break;
+ }
+ }
+ save_weights(net, outfile);
+}
+
+void reset_normalize_net(char *cfgfile, char *weightfile, char *outfile)
+{
+ gpu_index = -1;
+ network net = parse_network_cfg(cfgfile);
+ if (weightfile) {
+ load_weights(&net, weightfile);
+ }
+ int i;
+ for (i = 0; i < net.n; ++i) {
+ layer l = net.layers[i];
+ if (l.type == CONVOLUTIONAL && l.batch_normalize) {
+ denormalize_convolutional_layer(l);
+ }
+ if (l.type == CONNECTED && l.batch_normalize) {
+ denormalize_connected_layer(l);
+ }
+ if (l.type == GRU && l.batch_normalize) {
+ denormalize_connected_layer(*l.input_z_layer);
+ denormalize_connected_layer(*l.input_r_layer);
+ denormalize_connected_layer(*l.input_h_layer);
+ denormalize_connected_layer(*l.state_z_layer);
+ denormalize_connected_layer(*l.state_r_layer);
+ denormalize_connected_layer(*l.state_h_layer);
+ }
+ }
+ save_weights(net, outfile);
+}
+
+layer normalize_layer(layer l, int n)
+{
+ int j;
+ l.batch_normalize=1;
+ l.scales = calloc(n, sizeof(float));
+ for(j = 0; j < n; ++j){
+ l.scales[j] = 1;
+ }
+ l.rolling_mean = calloc(n, sizeof(float));
+ l.rolling_variance = calloc(n, sizeof(float));
+ return l;
+}
+
+void normalize_net(char *cfgfile, char *weightfile, char *outfile)
+{
+ gpu_index = -1;
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ int i;
+ for(i = 0; i < net.n; ++i){
+ layer l = net.layers[i];
+ if(l.type == CONVOLUTIONAL && !l.batch_normalize){
+ net.layers[i] = normalize_layer(l, l.n);
+ }
+ if (l.type == CONNECTED && !l.batch_normalize) {
+ net.layers[i] = normalize_layer(l, l.outputs);
+ }
+ if (l.type == GRU && l.batch_normalize) {
+ *l.input_z_layer = normalize_layer(*l.input_z_layer, l.input_z_layer->outputs);
+ *l.input_r_layer = normalize_layer(*l.input_r_layer, l.input_r_layer->outputs);
+ *l.input_h_layer = normalize_layer(*l.input_h_layer, l.input_h_layer->outputs);
+ *l.state_z_layer = normalize_layer(*l.state_z_layer, l.state_z_layer->outputs);
+ *l.state_r_layer = normalize_layer(*l.state_r_layer, l.state_r_layer->outputs);
+ *l.state_h_layer = normalize_layer(*l.state_h_layer, l.state_h_layer->outputs);
+ net.layers[i].batch_normalize=1;
+ }
+ }
+ save_weights(net, outfile);
+}
+
+void statistics_net(char *cfgfile, char *weightfile)
+{
+ gpu_index = -1;
+ network net = parse_network_cfg(cfgfile);
+ if (weightfile) {
+ load_weights(&net, weightfile);
+ }
+ int i;
+ for (i = 0; i < net.n; ++i) {
+ layer l = net.layers[i];
+ if (l.type == CONNECTED && l.batch_normalize) {
+ printf("Connected Layer %d\n", i);
+ statistics_connected_layer(l);
+ }
+ if (l.type == GRU && l.batch_normalize) {
+ printf("GRU Layer %d\n", i);
+ printf("Input Z\n");
+ statistics_connected_layer(*l.input_z_layer);
+ printf("Input R\n");
+ statistics_connected_layer(*l.input_r_layer);
+ printf("Input H\n");
+ statistics_connected_layer(*l.input_h_layer);
+ printf("State Z\n");
+ statistics_connected_layer(*l.state_z_layer);
+ printf("State R\n");
+ statistics_connected_layer(*l.state_r_layer);
+ printf("State H\n");
+ statistics_connected_layer(*l.state_h_layer);
+ }
+ printf("\n");
+ }
+}
+
+void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
+{
+ gpu_index = -1;
+ network net = parse_network_cfg(cfgfile);
+ if (weightfile) {
+ load_weights(&net, weightfile);
+ }
+ int i;
+ for (i = 0; i < net.n; ++i) {
+ layer l = net.layers[i];
+ if (l.type == CONVOLUTIONAL && l.batch_normalize) {
+ denormalize_convolutional_layer(l);
+ net.layers[i].batch_normalize=0;
+ }
+ if (l.type == CONNECTED && l.batch_normalize) {
+ denormalize_connected_layer(l);
+ net.layers[i].batch_normalize=0;
+ }
+ if (l.type == GRU && l.batch_normalize) {
+ denormalize_connected_layer(*l.input_z_layer);
+ denormalize_connected_layer(*l.input_r_layer);
+ denormalize_connected_layer(*l.input_h_layer);
+ denormalize_connected_layer(*l.state_z_layer);
+ denormalize_connected_layer(*l.state_r_layer);
+ denormalize_connected_layer(*l.state_h_layer);
+ l.input_z_layer->batch_normalize = 0;
+ l.input_r_layer->batch_normalize = 0;
+ l.input_h_layer->batch_normalize = 0;
+ l.state_z_layer->batch_normalize = 0;
+ l.state_r_layer->batch_normalize = 0;
+ l.state_h_layer->batch_normalize = 0;
+ net.layers[i].batch_normalize=0;
+ }
+ }
+ save_weights(net, outfile);
+}
+
+void visualize(char *cfgfile, char *weightfile)
+{
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ visualize_network(net);
+#ifdef OPENCV
+ cvWaitKey(0);
+#endif
}
int main(int argc, char **argv)
{
+#ifdef _DEBUG
+ _CrtSetDbgFlag(_CRTDBG_ALLOC_MEM_DF | _CRTDBG_LEAK_CHECK_DF);
+#endif
+
+ int i;
+ for (i = 0; i < argc; ++i) {
+ if (!argv[i]) continue;
+ strip_args(argv[i]);
+ }
+
+ //test_resize("data/bad.jpg");
+ //test_box();
//test_convolutional_layer();
if(argc < 2){
fprintf(stderr, "usage: %s <function>\n", argv[0]);
return 0;
}
gpu_index = find_int_arg(argc, argv, "-i", 0);
- if(find_arg(argc, argv, "-nogpu")) gpu_index = -1;
+ if(find_arg(argc, argv, "-nogpu")) {
+ gpu_index = -1;
+ }
#ifndef GPU
gpu_index = -1;
#else
if(gpu_index >= 0){
- cudaSetDevice(gpu_index);
+ cuda_set_device(gpu_index);
}
#endif
- if(0==strcmp(argv[1], "test_correct")) test_correct_alexnet();
- else if(0==strcmp(argv[1], "test_correct_nist")) test_correct_nist();
- //else if(0==strcmp(argv[1], "server")) run_server();
-
-#ifdef GPU
- else if(0==strcmp(argv[1], "test_gpu")) test_gpu_blas();
-#endif
-
- else if(argc < 3){
- fprintf(stderr, "usage: %s <function> <filename>\n", argv[0]);
- return 0;
+ if (0 == strcmp(argv[1], "average")){
+ average(argc, argv);
+ } else if (0 == strcmp(argv[1], "yolo")){
+ run_yolo(argc, argv);
+ } else if (0 == strcmp(argv[1], "voxel")){
+ run_voxel(argc, argv);
+ } else if (0 == strcmp(argv[1], "super")){
+ run_super(argc, argv);
+ } else if (0 == strcmp(argv[1], "detector")){
+ run_detector(argc, argv);
+ } else if (0 == strcmp(argv[1], "detect")){
+ float thresh = find_float_arg(argc, argv, "-thresh", .24);
+ int ext_output = find_arg(argc, argv, "-ext_output");
+ char *filename = (argc > 4) ? argv[4]: 0;
+ test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, ext_output);
+ } else if (0 == strcmp(argv[1], "cifar")){
+ run_cifar(argc, argv);
+ } else if (0 == strcmp(argv[1], "go")){
+ run_go(argc, argv);
+ } else if (0 == strcmp(argv[1], "rnn")){
+ run_char_rnn(argc, argv);
+ } else if (0 == strcmp(argv[1], "vid")){
+ run_vid_rnn(argc, argv);
+ } else if (0 == strcmp(argv[1], "coco")){
+ run_coco(argc, argv);
+ } else if (0 == strcmp(argv[1], "classify")){
+ predict_classifier("cfg/imagenet1k.data", argv[2], argv[3], argv[4], 5);
+ } else if (0 == strcmp(argv[1], "classifier")){
+ run_classifier(argc, argv);
+ } else if (0 == strcmp(argv[1], "art")){
+ run_art(argc, argv);
+ } else if (0 == strcmp(argv[1], "tag")){
+ run_tag(argc, argv);
+ } else if (0 == strcmp(argv[1], "compare")){
+ run_compare(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], "3d")){
+ composite_3d(argv[2], argv[3], argv[4], (argc > 5) ? atof(argv[5]) : 0);
+ } else if (0 == strcmp(argv[1], "test")){
+ test_resize(argv[2]);
+ } else if (0 == strcmp(argv[1], "captcha")){
+ run_captcha(argc, argv);
+ } else if (0 == strcmp(argv[1], "nightmare")){
+ run_nightmare(argc, argv);
+ } else if (0 == strcmp(argv[1], "rgbgr")){
+ rgbgr_net(argv[2], argv[3], argv[4]);
+ } else if (0 == strcmp(argv[1], "reset")){
+ reset_normalize_net(argv[2], argv[3], argv[4]);
+ } else if (0 == strcmp(argv[1], "denormalize")){
+ denormalize_net(argv[2], argv[3], argv[4]);
+ } else if (0 == strcmp(argv[1], "statistics")){
+ statistics_net(argv[2], argv[3]);
+ } else if (0 == strcmp(argv[1], "normalize")){
+ normalize_net(argv[2], argv[3], argv[4]);
+ } else if (0 == strcmp(argv[1], "rescale")){
+ rescale_net(argv[2], argv[3], argv[4]);
+ } else if (0 == strcmp(argv[1], "ops")){
+ operations(argv[2]);
+ } else if (0 == strcmp(argv[1], "speed")){
+ speed(argv[2], (argc > 3 && argv[3]) ? atoi(argv[3]) : 0);
+ } else if (0 == strcmp(argv[1], "oneoff")){
+ oneoff(argv[2], argv[3], argv[4]);
+ } else if (0 == strcmp(argv[1], "partial")){
+ partial(argv[2], argv[3], argv[4], atoi(argv[5]));
+ } else if (0 == strcmp(argv[1], "average")){
+ average(argc, argv);
+ } else if (0 == strcmp(argv[1], "visualize")){
+ visualize(argv[2], (argc > 3) ? argv[3] : 0);
+ } else if (0 == strcmp(argv[1], "imtest")){
+ test_resize(argv[2]);
+ } else {
+ fprintf(stderr, "Not an option: %s\n", argv[1]);
}
- 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;
}
--
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