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it's raining really hard outside :-( :rain: :storm: ☁️

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This commit is contained in:
Joseph Redmon
2017-10-17 11:41:34 -07:00
parent 532c6e1481
commit cd5d393b46
27 changed files with 1340 additions and 1669 deletions
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168
examples/darknet.c
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@@ -6,20 +6,15 @@
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, float hier_thresh, char *outfile, int fullscreen);
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_regressor(int argc, char **argv);
extern void run_segmenter(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);
@@ -32,20 +27,20 @@ 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);
network *net = parse_network_cfg(cfgfile);
network *sum = parse_network_cfg(cfgfile);
char *weightfile = argv[4];
load_weights(&sum, weightfile);
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];
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);
@@ -63,8 +58,8 @@ void average(int argc, char *argv[])
}
}
n = n+1;
for(j = 0; j < net.n; ++j){
layer l = sum.layers[j];
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);
@@ -83,12 +78,12 @@ void average(int argc, char *argv[])
save_weights(sum, outfile);
}
long numops(network net)
long numops(network *net)
{
int i;
long ops = 0;
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
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.groups * l.out_h*l.out_w;
} else if(l.type == CONNECTED){
@@ -121,11 +116,11 @@ long numops(network net)
void speed(char *cfgfile, int tics)
{
if (tics == 0) tics = 1000;
network net = parse_network_cfg(cfgfile);
set_batch_network(&net, 1);
network *net = parse_network_cfg(cfgfile);
set_batch_network(net, 1);
int i;
double time=what_time_is_it_now();
image im = make_image(net.w, net.h, net.c*net.batch);
image im = make_image(net->w, net->h, net->c*net->batch);
for(i = 0; i < tics; ++i){
network_predict(net, im.data);
}
@@ -141,7 +136,7 @@ void speed(char *cfgfile, int tics)
void operations(char *cfgfile)
{
gpu_index = -1;
network net = parse_network_cfg(cfgfile);
network *net = parse_network_cfg(cfgfile);
long ops = numops(net);
printf("Floating Point Operations: %ld\n", ops);
printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.);
@@ -150,63 +145,56 @@ void operations(char *cfgfile)
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;
scal_cpu(oldn*c, .1, net.layers[net.n - 2].weights, 1);
scal_cpu(oldn, 0, net.layers[net.n - 2].biases, 1);
net.layers[net.n - 2].n = 11921;
net.layers[net.n - 2].biases += 5;
net.layers[net.n - 2].weights += 5*c;
network *net = parse_network_cfg(cfgfile);
int oldn = net->layers[net->n - 2].n;
int c = net->layers[net->n - 2].c;
scal_cpu(oldn*c, .1, net->layers[net->n - 2].weights, 1);
scal_cpu(oldn, 0, net->layers[net->n - 2].biases, 1);
net->layers[net->n - 2].n = 11921;
net->layers[net->n - 2].biases += 5;
net->layers[net->n - 2].weights += 5*c;
if(weightfile){
load_weights(&net, 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;
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];
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->seen = 0;
save_weights(net, outfile);
}
void oneoff2(char *cfgfile, char *weightfile, char *outfile, int l)
{
gpu_index = -1;
network net = parse_network_cfg(cfgfile);
network *net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights_upto(&net, weightfile, 0, net.n);
load_weights_upto(&net, weightfile, l, net.n);
load_weights_upto(net, weightfile, 0, net->n);
load_weights_upto(net, weightfile, l, net->n);
}
*net.seen = 0;
save_weights_upto(net, outfile, net.n);
*net->seen = 0;
save_weights_upto(net, outfile, net->n);
}
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, 0, max);
}
*net.seen = 0;
network *net = load_network(cfgfile, weightfile, 1);
save_weights_upto(net, outfile, max);
}
void rescale_net(char *cfgfile, char *weightfile, char *outfile)
{
gpu_index = -1;
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
network *net = load_network(cfgfile, weightfile, 0);
int i;
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
for(i = 0; i < net->n; ++i){
layer l = net->layers[i];
if(l.type == CONVOLUTIONAL){
rescale_weights(l, 2, -.5);
break;
@@ -218,13 +206,10 @@ void rescale_net(char *cfgfile, char *weightfile, char *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);
}
network *net = load_network(cfgfile, weightfile, 0);
int i;
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
for(i = 0; i < net->n; ++i){
layer l = net->layers[i];
if(l.type == CONVOLUTIONAL){
rgbgr_weights(l);
break;
@@ -236,13 +221,10 @@ void rgbgr_net(char *cfgfile, char *weightfile, char *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);
}
network *net = load_network(cfgfile, weightfile, 0);
int i;
for (i = 0; i < net.n; ++i) {
layer l = net.layers[i];
for (i = 0; i < net->n; ++i) {
layer l = net->layers[i];
if (l.type == CONVOLUTIONAL && l.batch_normalize) {
denormalize_convolutional_layer(l);
}
@@ -277,18 +259,15 @@ layer normalize_layer(layer l, int n)
void normalize_net(char *cfgfile, char *weightfile, char *outfile)
{
gpu_index = -1;
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
network *net = load_network(cfgfile, weightfile, 0);
int i;
for(i = 0; i < net.n; ++i){
layer l = net.layers[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);
net->layers[i] = normalize_layer(l, l.n);
}
if (l.type == CONNECTED && !l.batch_normalize) {
net.layers[i] = normalize_layer(l, l.outputs);
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);
@@ -297,7 +276,7 @@ void normalize_net(char *cfgfile, char *weightfile, char *outfile)
*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;
net->layers[i].batch_normalize=1;
}
}
save_weights(net, outfile);
@@ -306,13 +285,10 @@ void normalize_net(char *cfgfile, char *weightfile, char *outfile)
void statistics_net(char *cfgfile, char *weightfile)
{
gpu_index = -1;
network net = parse_network_cfg(cfgfile);
if (weightfile) {
load_weights(&net, weightfile);
}
network *net = load_network(cfgfile, weightfile, 0);
int i;
for (i = 0; i < net.n; ++i) {
layer l = net.layers[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);
@@ -339,20 +315,17 @@ void statistics_net(char *cfgfile, char *weightfile)
void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
{
gpu_index = -1;
network net = parse_network_cfg(cfgfile);
if (weightfile) {
load_weights(&net, weightfile);
}
network *net = load_network(cfgfile, weightfile, 0);
int i;
for (i = 0; i < net.n; ++i) {
layer l = net.layers[i];
for (i = 0; i < net->n; ++i) {
layer l = net->layers[i];
if ((l.type == DECONVOLUTIONAL || l.type == CONVOLUTIONAL) && l.batch_normalize) {
denormalize_convolutional_layer(l);
net.layers[i].batch_normalize=0;
net->layers[i].batch_normalize=0;
}
if (l.type == CONNECTED && l.batch_normalize) {
denormalize_connected_layer(l);
net.layers[i].batch_normalize=0;
net->layers[i].batch_normalize=0;
}
if (l.type == GRU && l.batch_normalize) {
denormalize_connected_layer(*l.input_z_layer);
@@ -367,7 +340,7 @@ void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
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;
net->layers[i].batch_normalize=0;
}
}
save_weights(net, outfile);
@@ -375,9 +348,9 @@ void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
void mkimg(char *cfgfile, char *weightfile, int h, int w, int num, char *prefix)
{
network net = load_network(cfgfile, weightfile, 0);
image *ims = get_weights(net.layers[0]);
int n = net.layers[0].n;
network *net = load_network(cfgfile, weightfile, 0);
image *ims = get_weights(net->layers[0]);
int n = net->layers[0].n;
int z;
for(z = 0; z < num; ++z){
image im = make_image(h, w, 3);
@@ -401,10 +374,7 @@ void mkimg(char *cfgfile, char *weightfile, int h, int w, int num, char *prefix)
void visualize(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
network *net = load_network(cfgfile, weightfile, 0);
visualize_network(net);
#ifdef OPENCV
cvWaitKey(0);
@@ -437,8 +407,6 @@ int main(int argc, char **argv)
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], "lsd")){
@@ -457,8 +425,6 @@ int main(int argc, char **argv)
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")){
@@ -473,12 +439,6 @@ int main(int argc, char **argv)
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")){