From 23cb35e6c8eae8b59fab161036ae3f417a55c8db Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Fri, 30 Mar 2018 11:46:51 +0000
Subject: [PATCH] Changed small_object
---
src/darknet.c | 122 ++++++++++++++++++++++++++++------------
1 files changed, 86 insertions(+), 36 deletions(-)
diff --git a/src/darknet.c b/src/darknet.c
index 263349e..627b6db 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -12,6 +12,8 @@
#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);
extern void run_voxel(int argc, char **argv);
extern void run_yolo(int argc, char **argv);
extern void run_detector(int argc, char **argv);
@@ -30,20 +32,6 @@
extern void run_art(int argc, char **argv);
extern void run_super(int argc, char **argv);
-void change_rate(char *filename, float scale, float add)
-{
- // 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);
-}
-
void average(int argc, char *argv[])
{
char *cfgfile = argv[2];
@@ -66,7 +54,12 @@
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.filters, 1, out.filters, 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);
@@ -80,7 +73,12 @@
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.filters, 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);
@@ -125,6 +123,31 @@
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;
@@ -136,17 +159,6 @@
save_weights_upto(net, outfile, max);
}
-void stacked(char *cfgfile, char *weightfile, char *outfile)
-{
- gpu_index = -1;
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- net.seen = 0;
- save_weights_double(net, outfile);
-}
-
#include "convolutional_layer.h"
void rescale_net(char *cfgfile, char *weightfile, char *outfile)
{
@@ -159,7 +171,7 @@
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
- rescale_filters(l, 2, -.5);
+ rescale_weights(l, 2, -.5);
break;
}
}
@@ -177,7 +189,7 @@
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
- rgbgr_filters(l);
+ rgbgr_weights(l);
break;
}
}
@@ -254,6 +266,39 @@
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;
@@ -321,8 +366,7 @@
gpu_index = -1;
#else
if(gpu_index >= 0){
- cudaError_t status = cudaSetDevice(gpu_index);
- check_error(status);
+ cuda_set_device(gpu_index);
}
#endif
@@ -336,6 +380,10 @@
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")){
@@ -346,6 +394,8 @@
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")){
@@ -366,14 +416,14 @@
run_captcha(argc, argv);
} else if (0 == strcmp(argv[1], "nightmare")){
run_nightmare(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], "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")){
@@ -381,13 +431,13 @@
} else if (0 == strcmp(argv[1], "ops")){
operations(argv[2]);
} else if (0 == strcmp(argv[1], "speed")){
- speed(argv[2], (argc > 3) ? atoi(argv[3]) : 0);
+ 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], "stacked")){
- stacked(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "visualize")){
visualize(argv[2], (argc > 3) ? argv[3] : 0);
} else if (0 == strcmp(argv[1], "imtest")){
--
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