From ea09a6e0b38e1ddf43ffcd81d27f0506411eb8e4 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Tue, 09 Jan 2018 19:26:54 +0000
Subject: [PATCH] Command line for example of usage DLL/SO
---
src/darknet.c | 432 ++++++++++++++++++++++++++++++++++++++++++++++-------
1 files changed, 375 insertions(+), 57 deletions(-)
diff --git a/src/darknet.c b/src/darknet.c
index 0cd6153..627b6db 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -5,80 +5,335 @@
#include "parser.h"
#include "utils.h"
#include "cuda.h"
+#include "blas.h"
+#include "connected_layer.h"
-#define _GNU_SOURCE
-#include <fenv.h>
+#ifdef OPENCV
+#include "opencv2/highgui/highgui_c.h"
+#endif
-extern void run_imagenet(int argc, char **argv);
-extern void run_detection(int argc, char **argv);
+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);
+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 del_arg(int argc, char **argv, int index)
+void average(int argc, char *argv[])
{
- int i;
- for(i = index; i < argc-1; ++i) argv[i] = argv[i+1];
- argv[i] = 0;
-}
+ char *cfgfile = argv[2];
+ char *outfile = argv[3];
+ gpu_index = -1;
+ network net = parse_network_cfg(cfgfile);
+ network sum = parse_network_cfg(cfgfile);
-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;
+ char *weightfile = argv[4];
+ load_weights(&sum, weightfile);
+
+ int i, j;
+ int n = argc - 5;
+ for(i = 0; i < n; ++i){
+ 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;
-}
-
-int find_int_arg(int argc, char **argv, char *arg, int def)
-{
- 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);
- break;
+ 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);
}
}
- return def;
+ save_weights(sum, outfile);
}
-void change_rate(char *filename, float scale, float add)
+void speed(char *cfgfile, int tics)
{
- // 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+add);
- rate = rate*scale + add;
- fseek(fp, 0, SEEK_SET);
- fwrite(&rate, sizeof(float), 1, fp);
- fclose(fp);
+ if (tics == 0) tics = 1000;
+ network net = parse_network_cfg(cfgfile);
+ set_batch_network(&net, 1);
+ int 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(net, outfile);
+ *net.seen = 0;
+ save_weights_upto(net, outfile, max);
}
-void convert(char *cfgfile, char *outfile, char *weightfile)
+#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);
}
- save_network(net, outfile);
+ int i;
+ for(i = 0; i < net.n; ++i){
+ layer l = net.layers[i];
+ if(l.type == CONVOLUTIONAL){
+ rescale_weights(l, 2, -.5);
+ break;
+ }
+ }
+ save_weights(net, outfile);
+}
+
+void rgbgr_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){
+ 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)
@@ -88,42 +343,105 @@
load_weights(&net, weightfile);
}
visualize_network(net);
+#ifdef OPENCV
cvWaitKey(0);
+#endif
}
int main(int argc, char **argv)
{
- //test_resize(argv[1]);
+ //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], "imagenet")){
- run_imagenet(argc, argv);
- } else if (0 == strcmp(argv[1], "detection")){
- run_detection(argc, argv);
+ 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);
+ char *filename = (argc > 4) ? argv[4]: 0;
+ test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh);
+ } 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], "change")){
- change_rate(argv[2], atof(argv[3]), (argc > 4) ? atof(argv[4]) : 0);
- } else if (0 == strcmp(argv[1], "convert")){
- convert(argv[2], argv[3], (argc > 4) ? argv[4] : 0);
+ } 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]);
}
--
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