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/cifar.c | 542 +++++++++++++++++++++++++++---------------------------
1 files changed, 271 insertions(+), 271 deletions(-)
diff --git a/lib/detecter_tools/darknet/cifar.c b/lib/detecter_tools/darknet/cifar.c
index 011777f..1b87cb5 100644
--- a/lib/detecter_tools/darknet/cifar.c
+++ b/lib/detecter_tools/darknet/cifar.c
@@ -1,271 +1,271 @@
-#include "network.h"
-#include "utils.h"
-#include "parser.h"
-#include "option_list.h"
-#include "blas.h"
-
-void train_cifar(char *cfgfile, char *weightfile)
-{
- srand(time(0));
- float avg_loss = -1;
- char *base = basecfg(cfgfile);
- printf("%s\n", base);
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
-
- char* backup_directory = "backup/";
- int classes = 10;
- int N = 50000;
-
- char **labels = get_labels("data/cifar/labels.txt");
- int epoch = (*net.seen)/N;
- data train = load_all_cifar10();
- while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
- clock_t time=clock();
-
- float loss = train_network_sgd(net, train, 1);
- if(avg_loss == -1) avg_loss = loss;
- avg_loss = avg_loss*.95 + loss*.05;
- printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
- if(*net.seen/N > epoch){
- epoch = *net.seen/N;
- char buff[256];
- sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
- save_weights(net, buff);
- }
- if(get_current_batch(net)%100 == 0){
- char buff[256];
- sprintf(buff, "%s/%s.backup",backup_directory,base);
- save_weights(net, buff);
- }
- }
- char buff[256];
- sprintf(buff, "%s/%s.weights", backup_directory, base);
- save_weights(net, buff);
-
- free_network(net);
- free_ptrs((void**)labels, classes);
- free(base);
- free_data(train);
-}
-
-void train_cifar_distill(char *cfgfile, char *weightfile)
-{
- srand(time(0));
- float avg_loss = -1;
- char *base = basecfg(cfgfile);
- printf("%s\n", base);
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
-
- char* backup_directory = "backup/";
- int classes = 10;
- int N = 50000;
-
- char **labels = get_labels("data/cifar/labels.txt");
- int epoch = (*net.seen)/N;
-
- data train = load_all_cifar10();
- matrix soft = csv_to_matrix("results/ensemble.csv");
-
- float weight = .9;
- scale_matrix(soft, weight);
- scale_matrix(train.y, 1. - weight);
- matrix_add_matrix(soft, train.y);
-
- while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
- clock_t time=clock();
-
- float loss = train_network_sgd(net, train, 1);
- if(avg_loss == -1) avg_loss = loss;
- avg_loss = avg_loss*.95 + loss*.05;
- printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
- if(*net.seen/N > epoch){
- epoch = *net.seen/N;
- char buff[256];
- sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
- save_weights(net, buff);
- }
- if(get_current_batch(net)%100 == 0){
- char buff[256];
- sprintf(buff, "%s/%s.backup",backup_directory,base);
- save_weights(net, buff);
- }
- }
- char buff[256];
- sprintf(buff, "%s/%s.weights", backup_directory, base);
- save_weights(net, buff);
-
- free_network(net);
- free_ptrs((void**)labels, classes);
- free(base);
- free_data(train);
-}
-
-void test_cifar_multi(char *filename, char *weightfile)
-{
- network net = parse_network_cfg(filename);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- set_batch_network(&net, 1);
- srand(time(0));
-
- float avg_acc = 0;
- data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
-
- int i;
- for(i = 0; i < test.X.rows; ++i){
- image im = float_to_image(32, 32, 3, test.X.vals[i]);
-
- float pred[10] = {0};
-
- float *p = network_predict(net, im.data);
- axpy_cpu(10, 1, p, 1, pred, 1);
- flip_image(im);
- p = network_predict(net, im.data);
- axpy_cpu(10, 1, p, 1, pred, 1);
-
- int index = max_index(pred, 10);
- int class_id = max_index(test.y.vals[i], 10);
- if(index == class_id) avg_acc += 1;
- free_image(im);
- printf("%4d: %.2f%%\n", i, 100.*avg_acc/(i+1));
- }
-}
-
-void test_cifar(char *filename, char *weightfile)
-{
- network net = parse_network_cfg(filename);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- srand(time(0));
-
- clock_t time;
- float avg_acc = 0;
- float avg_top5 = 0;
- data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
-
- time=clock();
-
- float *acc = network_accuracies(net, test, 2);
- avg_acc += acc[0];
- avg_top5 += acc[1];
- printf("top1: %f, %lf seconds, %d images\n", avg_acc, sec(clock()-time), test.X.rows);
- free_data(test);
-}
-
-void extract_cifar()
-{
-char *labels[] = {"airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"};
- int i;
- data train = load_all_cifar10();
- data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
- for(i = 0; i < train.X.rows; ++i){
- image im = float_to_image(32, 32, 3, train.X.vals[i]);
- int class_id = max_index(train.y.vals[i], 10);
- char buff[256];
- sprintf(buff, "data/cifar/train/%d_%s",i,labels[class_id]);
- save_image_png(im, buff);
- }
- for(i = 0; i < test.X.rows; ++i){
- image im = float_to_image(32, 32, 3, test.X.vals[i]);
- int class_id = max_index(test.y.vals[i], 10);
- char buff[256];
- sprintf(buff, "data/cifar/test/%d_%s",i,labels[class_id]);
- save_image_png(im, buff);
- }
-}
-
-void test_cifar_csv(char *filename, char *weightfile)
-{
- network net = parse_network_cfg(filename);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- srand(time(0));
-
- data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
-
- matrix pred = network_predict_data(net, test);
-
- int i;
- for(i = 0; i < test.X.rows; ++i){
- image im = float_to_image(32, 32, 3, test.X.vals[i]);
- flip_image(im);
- }
- matrix pred2 = network_predict_data(net, test);
- scale_matrix(pred, .5);
- scale_matrix(pred2, .5);
- matrix_add_matrix(pred2, pred);
-
- matrix_to_csv(pred);
- fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
- free_data(test);
-}
-
-void test_cifar_csvtrain(char *filename, char *weightfile)
-{
- network net = parse_network_cfg(filename);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- srand(time(0));
-
- data test = load_all_cifar10();
-
- matrix pred = network_predict_data(net, test);
-
- int i;
- for(i = 0; i < test.X.rows; ++i){
- image im = float_to_image(32, 32, 3, test.X.vals[i]);
- flip_image(im);
- }
- matrix pred2 = network_predict_data(net, test);
- scale_matrix(pred, .5);
- scale_matrix(pred2, .5);
- matrix_add_matrix(pred2, pred);
-
- matrix_to_csv(pred);
- fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
- free_data(test);
-}
-
-void eval_cifar_csv()
-{
- data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
-
- matrix pred = csv_to_matrix("results/combined.csv");
- fprintf(stderr, "%d %d\n", pred.rows, pred.cols);
-
- fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
- free_data(test);
- free_matrix(pred);
-}
-
-
-void run_cifar(int argc, char **argv)
-{
- if(argc < 4){
- fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
- return;
- }
-
- char *cfg = argv[3];
- char *weights = (argc > 4) ? argv[4] : 0;
- if(0==strcmp(argv[2], "train")) train_cifar(cfg, weights);
- else if(0==strcmp(argv[2], "extract")) extract_cifar();
- else if(0==strcmp(argv[2], "distill")) train_cifar_distill(cfg, weights);
- else if(0==strcmp(argv[2], "test")) test_cifar(cfg, weights);
- else if(0==strcmp(argv[2], "multi")) test_cifar_multi(cfg, weights);
- else if(0==strcmp(argv[2], "csv")) test_cifar_csv(cfg, weights);
- else if(0==strcmp(argv[2], "csvtrain")) test_cifar_csvtrain(cfg, weights);
- else if(0==strcmp(argv[2], "eval")) eval_cifar_csv();
-}
+#include "network.h"
+#include "utils.h"
+#include "parser.h"
+#include "option_list.h"
+#include "blas.h"
+
+void train_cifar(char *cfgfile, char *weightfile)
+{
+ srand(time(0));
+ float avg_loss = -1;
+ char *base = basecfg(cfgfile);
+ printf("%s\n", base);
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+
+ char* backup_directory = "backup/";
+ int classes = 10;
+ int N = 50000;
+
+ char **labels = get_labels("data/cifar/labels.txt");
+ int epoch = (*net.seen)/N;
+ data train = load_all_cifar10();
+ while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
+ clock_t time=clock();
+
+ float loss = train_network_sgd(net, train, 1);
+ if(avg_loss == -1) avg_loss = loss;
+ avg_loss = avg_loss*.95 + loss*.05;
+ printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
+ if(*net.seen/N > epoch){
+ epoch = *net.seen/N;
+ char buff[256];
+ sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+ save_weights(net, buff);
+ }
+ if(get_current_batch(net)%100 == 0){
+ char buff[256];
+ sprintf(buff, "%s/%s.backup",backup_directory,base);
+ save_weights(net, buff);
+ }
+ }
+ char buff[256];
+ sprintf(buff, "%s/%s.weights", backup_directory, base);
+ save_weights(net, buff);
+
+ free_network(net);
+ free_ptrs((void**)labels, classes);
+ free(base);
+ free_data(train);
+}
+
+void train_cifar_distill(char *cfgfile, char *weightfile)
+{
+ srand(time(0));
+ float avg_loss = -1;
+ char *base = basecfg(cfgfile);
+ printf("%s\n", base);
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+
+ char* backup_directory = "backup/";
+ int classes = 10;
+ int N = 50000;
+
+ char **labels = get_labels("data/cifar/labels.txt");
+ int epoch = (*net.seen)/N;
+
+ data train = load_all_cifar10();
+ matrix soft = csv_to_matrix("results/ensemble.csv");
+
+ float weight = .9;
+ scale_matrix(soft, weight);
+ scale_matrix(train.y, 1. - weight);
+ matrix_add_matrix(soft, train.y);
+
+ while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
+ clock_t time=clock();
+
+ float loss = train_network_sgd(net, train, 1);
+ if(avg_loss == -1) avg_loss = loss;
+ avg_loss = avg_loss*.95 + loss*.05;
+ printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
+ if(*net.seen/N > epoch){
+ epoch = *net.seen/N;
+ char buff[256];
+ sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+ save_weights(net, buff);
+ }
+ if(get_current_batch(net)%100 == 0){
+ char buff[256];
+ sprintf(buff, "%s/%s.backup",backup_directory,base);
+ save_weights(net, buff);
+ }
+ }
+ char buff[256];
+ sprintf(buff, "%s/%s.weights", backup_directory, base);
+ save_weights(net, buff);
+
+ free_network(net);
+ free_ptrs((void**)labels, classes);
+ free(base);
+ free_data(train);
+}
+
+void test_cifar_multi(char *filename, char *weightfile)
+{
+ network net = parse_network_cfg(filename);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ srand(time(0));
+
+ float avg_acc = 0;
+ data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
+
+ int i;
+ for(i = 0; i < test.X.rows; ++i){
+ image im = float_to_image(32, 32, 3, test.X.vals[i]);
+
+ float pred[10] = {0};
+
+ float *p = network_predict(net, im.data);
+ axpy_cpu(10, 1, p, 1, pred, 1);
+ flip_image(im);
+ p = network_predict(net, im.data);
+ axpy_cpu(10, 1, p, 1, pred, 1);
+
+ int index = max_index(pred, 10);
+ int class_id = max_index(test.y.vals[i], 10);
+ if(index == class_id) avg_acc += 1;
+ free_image(im);
+ printf("%4d: %.2f%%\n", i, 100.*avg_acc/(i+1));
+ }
+}
+
+void test_cifar(char *filename, char *weightfile)
+{
+ network net = parse_network_cfg(filename);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ clock_t time;
+ float avg_acc = 0;
+ float avg_top5 = 0;
+ data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
+
+ time=clock();
+
+ float *acc = network_accuracies(net, test, 2);
+ avg_acc += acc[0];
+ avg_top5 += acc[1];
+ printf("top1: %f, %lf seconds, %d images\n", avg_acc, sec(clock()-time), test.X.rows);
+ free_data(test);
+}
+
+void extract_cifar()
+{
+char *labels[] = {"airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"};
+ int i;
+ data train = load_all_cifar10();
+ data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
+ for(i = 0; i < train.X.rows; ++i){
+ image im = float_to_image(32, 32, 3, train.X.vals[i]);
+ int class_id = max_index(train.y.vals[i], 10);
+ char buff[256];
+ sprintf(buff, "data/cifar/train/%d_%s",i,labels[class_id]);
+ save_image_png(im, buff);
+ }
+ for(i = 0; i < test.X.rows; ++i){
+ image im = float_to_image(32, 32, 3, test.X.vals[i]);
+ int class_id = max_index(test.y.vals[i], 10);
+ char buff[256];
+ sprintf(buff, "data/cifar/test/%d_%s",i,labels[class_id]);
+ save_image_png(im, buff);
+ }
+}
+
+void test_cifar_csv(char *filename, char *weightfile)
+{
+ network net = parse_network_cfg(filename);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
+
+ matrix pred = network_predict_data(net, test);
+
+ int i;
+ for(i = 0; i < test.X.rows; ++i){
+ image im = float_to_image(32, 32, 3, test.X.vals[i]);
+ flip_image(im);
+ }
+ matrix pred2 = network_predict_data(net, test);
+ scale_matrix(pred, .5);
+ scale_matrix(pred2, .5);
+ matrix_add_matrix(pred2, pred);
+
+ matrix_to_csv(pred);
+ fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
+ free_data(test);
+}
+
+void test_cifar_csvtrain(char *filename, char *weightfile)
+{
+ network net = parse_network_cfg(filename);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ srand(time(0));
+
+ data test = load_all_cifar10();
+
+ matrix pred = network_predict_data(net, test);
+
+ int i;
+ for(i = 0; i < test.X.rows; ++i){
+ image im = float_to_image(32, 32, 3, test.X.vals[i]);
+ flip_image(im);
+ }
+ matrix pred2 = network_predict_data(net, test);
+ scale_matrix(pred, .5);
+ scale_matrix(pred2, .5);
+ matrix_add_matrix(pred2, pred);
+
+ matrix_to_csv(pred);
+ fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
+ free_data(test);
+}
+
+void eval_cifar_csv()
+{
+ data test = load_cifar10_data("data/cifar/cifar-10-batches-bin/test_batch.bin");
+
+ matrix pred = csv_to_matrix("results/combined.csv");
+ fprintf(stderr, "%d %d\n", pred.rows, pred.cols);
+
+ fprintf(stderr, "Accuracy: %f\n", matrix_topk_accuracy(test.y, pred, 1));
+ free_data(test);
+ free_matrix(pred);
+}
+
+
+void run_cifar(int argc, char **argv)
+{
+ if(argc < 4){
+ fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+ return;
+ }
+
+ char *cfg = argv[3];
+ char *weights = (argc > 4) ? argv[4] : 0;
+ if(0==strcmp(argv[2], "train")) train_cifar(cfg, weights);
+ else if(0==strcmp(argv[2], "extract")) extract_cifar();
+ else if(0==strcmp(argv[2], "distill")) train_cifar_distill(cfg, weights);
+ else if(0==strcmp(argv[2], "test")) test_cifar(cfg, weights);
+ else if(0==strcmp(argv[2], "multi")) test_cifar_multi(cfg, weights);
+ else if(0==strcmp(argv[2], "csv")) test_cifar_csv(cfg, weights);
+ else if(0==strcmp(argv[2], "csvtrain")) test_cifar_csvtrain(cfg, weights);
+ else if(0==strcmp(argv[2], "eval")) eval_cifar_csv();
+}
--
Gitblit v1.8.0