From 168af40fe9a3cc81c6ee16b3e81f154780c36bdb Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 03 六月 2021 15:03:27 +0800
Subject: [PATCH] up new v4
---
lib/detecter_tools/darknet/darknet.c | 1118 +++++++++++++++++++++++++++++-----------------------------
1 files changed, 559 insertions(+), 559 deletions(-)
diff --git a/lib/detecter_tools/darknet/darknet.c b/lib/detecter_tools/darknet/darknet.c
index d6500ed..6c52b10 100644
--- a/lib/detecter_tools/darknet/darknet.c
+++ b/lib/detecter_tools/darknet/darknet.c
@@ -1,559 +1,559 @@
-#include "darknet.h"
-#include <time.h>
-#include <stdlib.h>
-#include <stdio.h>
-#if defined(_MSC_VER) && defined(_DEBUG)
-#include <crtdbg.h>
-#endif
-
-#include "parser.h"
-#include "utils.h"
-#include "dark_cuda.h"
-#include "blas.h"
-#include "connected_layer.h"
-
-
-extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top);
-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[])
-{
- char *cfgfile = argv[2];
- char *outfile = argv[3];
- gpu_index = -1;
- network net = parse_network_cfg(cfgfile);
- network sum = parse_network_cfg(cfgfile);
-
- 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);
- }
- }
- }
- 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);
-}
-
-void speed(char *cfgfile, int tics)
-{
- 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;
- } else if (l.type == RNN){
- ops += 2l * l.input_layer->inputs * l.input_layer->outputs;
- ops += 2l * l.self_layer->inputs * l.self_layer->outputs;
- ops += 2l * l.output_layer->inputs * l.output_layer->outputs;
- } else if (l.type == GRU){
- ops += 2l * l.uz->inputs * l.uz->outputs;
- ops += 2l * l.uh->inputs * l.uh->outputs;
- ops += 2l * l.ur->inputs * l.ur->outputs;
- ops += 2l * l.wz->inputs * l.wz->outputs;
- ops += 2l * l.wh->inputs * l.wh->outputs;
- ops += 2l * l.wr->inputs * l.wr->outputs;
- } else if (l.type == LSTM){
- ops += 2l * l.uf->inputs * l.uf->outputs;
- ops += 2l * l.ui->inputs * l.ui->outputs;
- ops += 2l * l.ug->inputs * l.ug->outputs;
- ops += 2l * l.uo->inputs * l.uo->outputs;
- ops += 2l * l.wf->inputs * l.wf->outputs;
- ops += 2l * l.wi->inputs * l.wi->outputs;
- ops += 2l * l.wg->inputs * l.wg->outputs;
- ops += 2l * l.wo->inputs * l.wo->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;
- *net.cur_iteration = 0;
- save_weights(net, outfile);
-}
-
-void partial(char *cfgfile, char *weightfile, char *outfile, int max)
-{
- gpu_index = -1;
- network net = parse_network_cfg_custom(cfgfile, 1, 1);
- if(weightfile){
- load_weights_upto(&net, weightfile, max);
- }
- *net.seen = 0;
- *net.cur_iteration = 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;
- }
- }
- 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);
- }
- if (l.type == LSTM && l.batch_normalize) {
- denormalize_connected_layer(*l.wf);
- denormalize_connected_layer(*l.wi);
- denormalize_connected_layer(*l.wg);
- denormalize_connected_layer(*l.wo);
- denormalize_connected_layer(*l.uf);
- denormalize_connected_layer(*l.ui);
- denormalize_connected_layer(*l.ug);
- denormalize_connected_layer(*l.uo);
- }
- }
- save_weights(net, outfile);
-}
-
-layer normalize_layer(layer l, int n)
-{
- int j;
- l.batch_normalize=1;
- l.scales = (float*)xcalloc(n, sizeof(float));
- for(j = 0; j < n; ++j){
- l.scales[j] = 1;
- }
- l.rolling_mean = (float*)xcalloc(n, sizeof(float));
- l.rolling_variance = (float*)xcalloc(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;
- }
- if (l.type == LSTM && l.batch_normalize) {
- *l.wf = normalize_layer(*l.wf, l.wf->outputs);
- *l.wi = normalize_layer(*l.wi, l.wi->outputs);
- *l.wg = normalize_layer(*l.wg, l.wg->outputs);
- *l.wo = normalize_layer(*l.wo, l.wo->outputs);
- *l.uf = normalize_layer(*l.uf, l.uf->outputs);
- *l.ui = normalize_layer(*l.ui, l.ui->outputs);
- *l.ug = normalize_layer(*l.ug, l.ug->outputs);
- *l.uo = normalize_layer(*l.uo, l.uo->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);
- }
- if (l.type == LSTM && l.batch_normalize) {
- printf("LSTM Layer %d\n", i);
- printf("wf\n");
- statistics_connected_layer(*l.wf);
- printf("wi\n");
- statistics_connected_layer(*l.wi);
- printf("wg\n");
- statistics_connected_layer(*l.wg);
- printf("wo\n");
- statistics_connected_layer(*l.wo);
- printf("uf\n");
- statistics_connected_layer(*l.uf);
- printf("ui\n");
- statistics_connected_layer(*l.ui);
- printf("ug\n");
- statistics_connected_layer(*l.ug);
- printf("uo\n");
- statistics_connected_layer(*l.uo);
- }
- 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;
- }
- if (l.type == GRU && l.batch_normalize) {
- denormalize_connected_layer(*l.wf);
- denormalize_connected_layer(*l.wi);
- denormalize_connected_layer(*l.wg);
- denormalize_connected_layer(*l.wo);
- denormalize_connected_layer(*l.uf);
- denormalize_connected_layer(*l.ui);
- denormalize_connected_layer(*l.ug);
- denormalize_connected_layer(*l.uo);
- l.wf->batch_normalize = 0;
- l.wi->batch_normalize = 0;
- l.wg->batch_normalize = 0;
- l.wo->batch_normalize = 0;
- l.uf->batch_normalize = 0;
- l.ui->batch_normalize = 0;
- l.ug->batch_normalize = 0;
- l.uo->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
- wait_until_press_key_cv();
-#endif
-}
-
-//int main(int argc, char **argv)
-//{
-//#ifdef _DEBUG
-// _CrtSetDbgFlag(_CRTDBG_ALLOC_MEM_DF | _CRTDBG_LEAK_CHECK_DF);
-// printf(" _DEBUG is used \n");
-//#endif
-//
-//#ifdef DEBUG
-// printf(" DEBUG=1 \n");
-//#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;
-// printf("\n Currently Darknet doesn't support -nogpu flag. If you want to use CPU - please compile Darknet with GPU=0 in the Makefile, or compile darknet_no_gpu.sln on Windows.\n");
-// exit(-1);
-// }
-//
-//#ifndef GPU
-// gpu_index = -1;
-// printf(" GPU isn't used \n");
-// init_cpu();
-//#else // GPU
-// if(gpu_index >= 0){
-// cuda_set_device(gpu_index);
-// CHECK_CUDA(cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync));
-// }
-//
-// show_cuda_cudnn_info();
-// cuda_debug_sync = find_arg(argc, argv, "-cuda_debug_sync");
-//
-//#ifdef CUDNN_HALF
-// printf(" CUDNN_HALF=1 \n");
-//#endif // CUDNN_HALF
-//
-//#endif // GPU
-//
-// show_opencv_info();
-//
-// 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, 0.5, 0, ext_output, 0, NULL, 0, 0);
-// } 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], "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]);
-// }
-// return 0;
-//}
+#include "darknet.h"
+#include <time.h>
+#include <stdlib.h>
+#include <stdio.h>
+#if defined(_MSC_VER) && defined(_DEBUG)
+#include <crtdbg.h>
+#endif
+
+#include "parser.h"
+#include "utils.h"
+#include "dark_cuda.h"
+#include "blas.h"
+#include "connected_layer.h"
+
+
+extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top);
+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[])
+{
+ char *cfgfile = argv[2];
+ char *outfile = argv[3];
+ gpu_index = -1;
+ network net = parse_network_cfg(cfgfile);
+ network sum = parse_network_cfg(cfgfile);
+
+ 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);
+ }
+ }
+ }
+ 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);
+}
+
+void speed(char *cfgfile, int tics)
+{
+ 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;
+ } else if (l.type == RNN){
+ ops += 2l * l.input_layer->inputs * l.input_layer->outputs;
+ ops += 2l * l.self_layer->inputs * l.self_layer->outputs;
+ ops += 2l * l.output_layer->inputs * l.output_layer->outputs;
+ } else if (l.type == GRU){
+ ops += 2l * l.uz->inputs * l.uz->outputs;
+ ops += 2l * l.uh->inputs * l.uh->outputs;
+ ops += 2l * l.ur->inputs * l.ur->outputs;
+ ops += 2l * l.wz->inputs * l.wz->outputs;
+ ops += 2l * l.wh->inputs * l.wh->outputs;
+ ops += 2l * l.wr->inputs * l.wr->outputs;
+ } else if (l.type == LSTM){
+ ops += 2l * l.uf->inputs * l.uf->outputs;
+ ops += 2l * l.ui->inputs * l.ui->outputs;
+ ops += 2l * l.ug->inputs * l.ug->outputs;
+ ops += 2l * l.uo->inputs * l.uo->outputs;
+ ops += 2l * l.wf->inputs * l.wf->outputs;
+ ops += 2l * l.wi->inputs * l.wi->outputs;
+ ops += 2l * l.wg->inputs * l.wg->outputs;
+ ops += 2l * l.wo->inputs * l.wo->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;
+ *net.cur_iteration = 0;
+ save_weights(net, outfile);
+}
+
+void partial(char *cfgfile, char *weightfile, char *outfile, int max)
+{
+ gpu_index = -1;
+ network net = parse_network_cfg_custom(cfgfile, 1, 1);
+ if(weightfile){
+ load_weights_upto(&net, weightfile, max);
+ }
+ *net.seen = 0;
+ *net.cur_iteration = 0;
+ save_weights_upto(net, outfile, max, 0);
+}
+
+#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;
+ }
+ }
+ 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);
+ }
+ if (l.type == LSTM && l.batch_normalize) {
+ denormalize_connected_layer(*l.wf);
+ denormalize_connected_layer(*l.wi);
+ denormalize_connected_layer(*l.wg);
+ denormalize_connected_layer(*l.wo);
+ denormalize_connected_layer(*l.uf);
+ denormalize_connected_layer(*l.ui);
+ denormalize_connected_layer(*l.ug);
+ denormalize_connected_layer(*l.uo);
+ }
+ }
+ save_weights(net, outfile);
+}
+
+layer normalize_layer(layer l, int n)
+{
+ int j;
+ l.batch_normalize=1;
+ l.scales = (float*)xcalloc(n, sizeof(float));
+ for(j = 0; j < n; ++j){
+ l.scales[j] = 1;
+ }
+ l.rolling_mean = (float*)xcalloc(n, sizeof(float));
+ l.rolling_variance = (float*)xcalloc(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;
+ }
+ if (l.type == LSTM && l.batch_normalize) {
+ *l.wf = normalize_layer(*l.wf, l.wf->outputs);
+ *l.wi = normalize_layer(*l.wi, l.wi->outputs);
+ *l.wg = normalize_layer(*l.wg, l.wg->outputs);
+ *l.wo = normalize_layer(*l.wo, l.wo->outputs);
+ *l.uf = normalize_layer(*l.uf, l.uf->outputs);
+ *l.ui = normalize_layer(*l.ui, l.ui->outputs);
+ *l.ug = normalize_layer(*l.ug, l.ug->outputs);
+ *l.uo = normalize_layer(*l.uo, l.uo->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);
+ }
+ if (l.type == LSTM && l.batch_normalize) {
+ printf("LSTM Layer %d\n", i);
+ printf("wf\n");
+ statistics_connected_layer(*l.wf);
+ printf("wi\n");
+ statistics_connected_layer(*l.wi);
+ printf("wg\n");
+ statistics_connected_layer(*l.wg);
+ printf("wo\n");
+ statistics_connected_layer(*l.wo);
+ printf("uf\n");
+ statistics_connected_layer(*l.uf);
+ printf("ui\n");
+ statistics_connected_layer(*l.ui);
+ printf("ug\n");
+ statistics_connected_layer(*l.ug);
+ printf("uo\n");
+ statistics_connected_layer(*l.uo);
+ }
+ 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;
+ }
+ if (l.type == GRU && l.batch_normalize) {
+ denormalize_connected_layer(*l.wf);
+ denormalize_connected_layer(*l.wi);
+ denormalize_connected_layer(*l.wg);
+ denormalize_connected_layer(*l.wo);
+ denormalize_connected_layer(*l.uf);
+ denormalize_connected_layer(*l.ui);
+ denormalize_connected_layer(*l.ug);
+ denormalize_connected_layer(*l.uo);
+ l.wf->batch_normalize = 0;
+ l.wi->batch_normalize = 0;
+ l.wg->batch_normalize = 0;
+ l.wo->batch_normalize = 0;
+ l.uf->batch_normalize = 0;
+ l.ui->batch_normalize = 0;
+ l.ug->batch_normalize = 0;
+ l.uo->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
+ wait_until_press_key_cv();
+#endif
+}
+
+//int main(int argc, char **argv)
+//{
+//#ifdef _DEBUG
+// _CrtSetDbgFlag(_CRTDBG_ALLOC_MEM_DF | _CRTDBG_LEAK_CHECK_DF);
+// printf(" _DEBUG is used \n");
+//#endif
+//
+//#ifdef DEBUG
+// printf(" DEBUG=1 \n");
+//#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;
+// printf("\n Currently Darknet doesn't support -nogpu flag. If you want to use CPU - please compile Darknet with GPU=0 in the Makefile, or compile darknet_no_gpu.sln on Windows.\n");
+// exit(-1);
+// }
+//
+//#ifndef GPU
+// gpu_index = -1;
+// printf(" GPU isn't used \n");
+// init_cpu();
+//#else // GPU
+// if(gpu_index >= 0){
+// cuda_set_device(gpu_index);
+// CHECK_CUDA(cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync));
+// }
+//
+// show_cuda_cudnn_info();
+// cuda_debug_sync = find_arg(argc, argv, "-cuda_debug_sync");
+//
+//#ifdef CUDNN_HALF
+// printf(" CUDNN_HALF=1 \n");
+//#endif // CUDNN_HALF
+//
+//#endif // GPU
+//
+// show_opencv_info();
+//
+// 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, 0.5, 0, ext_output, 0, NULL, 0, 0);
+// } 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], "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]);
+// }
+// return 0;
+//}
--
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