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accidentally broke multigpu a while ago :-/

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This commit is contained in:
Joseph Redmon 2017-06-26 14:00:18 -07:00
parent 63ebd694d7
commit 59ed1719d4
12 changed files with 119 additions and 46 deletions
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2
cfg/gru.cfg
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@ -10,7 +10,7 @@ adam=1
policy=constant
power=4
max_batches=400000
max_batches=1000000
[gru]
output = 1024

1
examples/classifier.c
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@ -33,6 +33,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
cuda_set_device(gpus[i]);
#endif
nets[i] = load_network(cfgfile, weightfile, clear);
nets[i].learning_rate *= ngpus;
}
srand(time(0));
network net = nets[0];

2
examples/darknet.c
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@ -337,7 +337,7 @@ void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
int i;
for (i = 0; i < net.n; ++i) {
layer l = net.layers[i];
if (l.type == CONVOLUTIONAL && l.batch_normalize) {
if ((l.type == DECONVOLUTIONAL || l.type == CONVOLUTIONAL) && l.batch_normalize) {
denormalize_convolutional_layer(l);
net.layers[i].batch_normalize=0;
}

1
examples/detector.c
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@ -23,6 +23,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
cuda_set_device(gpus[i]);
#endif
nets[i] = load_network(cfgfile, weightfile, clear);
nets[i].learning_rate *= ngpus;
}
srand(time(0));
network net = nets[0];

3
examples/rnn.c
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@ -43,6 +43,7 @@ char **read_tokens(char *filename, size_t *read)
size = size*2;
d = realloc(d, size*sizeof(char *));
}
if(0==strcmp(line, "<NEWLINE>")) line = "\n";
d[count-1] = line;
}
fclose(fp);
@ -190,7 +191,7 @@ void train_char_rnn(char *cfgfile, char *weightfile, char *filename, int clear,
for(j = 0; j < streams; ++j){
//printf("%d\n", j);
if(rand()%10 == 0){
if(rand()%64 == 0){
//fprintf(stderr, "Reset\n");
offsets[j] = rand_size_t()%size;
reset_rnn_state(net, j);

33
examples/segmenter.c
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@ -2,7 +2,7 @@
#include <sys/time.h>
#include <assert.h>
void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int display)
{
int i;
@ -95,9 +95,9 @@ void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
#else
loss = train_network(net, train);
#endif
if(1){
image tr = float_to_image(net.w/div, net.h/div, 80, train.y.vals[net.batch]);
image im = float_to_image(net.w, net.h, net.c, train.X.vals[net.batch]);
if(display){
image tr = float_to_image(net.w/div, net.h/div, 80, train.y.vals[net.batch*(net.subdivisions-1)]);
image im = float_to_image(net.w, net.h, net.c, train.X.vals[net.batch*(net.subdivisions-1)]);
image mask = mask_to_rgb(tr);
image prmask = mask_to_rgb(pred);
show_image(im, "input");
@ -163,10 +163,10 @@ void predict_segmenter(char *datafile, char *cfgfile, char *weightfile, char *fi
float *X = sized.data;
time=clock();
float *predictions = network_predict(net, X);
image m = float_to_image(sized.w, sized.h, 81, predictions);
image rgb = mask_to_rgb(m);
image pred = get_network_image(net);
image prmask = mask_to_rgb(pred);
show_image(sized, "orig");
show_image(rgb, "pred");
show_image(prmask, "pred");
#ifdef OPENCV
cvWaitKey(0);
#endif
@ -174,7 +174,7 @@ void predict_segmenter(char *datafile, char *cfgfile, char *weightfile, char *fi
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
free_image(im);
free_image(sized);
free_image(rgb);
free_image(prmask);
if (filename) break;
}
}
@ -183,7 +183,7 @@ void predict_segmenter(char *datafile, char *cfgfile, char *weightfile, char *fi
void demo_segmenter(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
{
#ifdef OPENCV
printf("Regressor Demo\n");
printf("Classifier Demo\n");
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
@ -200,8 +200,8 @@ void demo_segmenter(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
}
if(!cap) error("Couldn't connect to webcam.\n");
cvNamedWindow("Regressor", CV_WINDOW_NORMAL);
cvResizeWindow("Regressor", 512, 512);
cvNamedWindow("Segmenter", CV_WINDOW_NORMAL);
cvResizeWindow("Segmenter", 512, 512);
float fps = 0;
while(1){
@ -210,7 +210,6 @@ void demo_segmenter(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
image in = get_image_from_stream(cap);
image in_s = letterbox_image(in, net.w, net.h);
show_image(in, "Regressor");
float *predictions = network_predict(net, in_s.data);
@ -218,10 +217,13 @@ void demo_segmenter(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
printf("\033[1;1H");
printf("\nFPS:%.0f\n",fps);
printf("People: %f\n", predictions[0]);
image pred = get_network_image(net);
image prmask = mask_to_rgb(pred);
show_image(prmask, "Segmenter");
free_image(in_s);
free_image(in);
free_image(prmask);
cvWaitKey(10);
@ -266,12 +268,13 @@ void run_segmenter(int argc, char **argv)
int cam_index = find_int_arg(argc, argv, "-c", 0);
int clear = find_arg(argc, argv, "-clear");
int display = find_arg(argc, argv, "-display");
char *data = argv[3];
char *cfg = argv[4];
char *weights = (argc > 5) ? argv[5] : 0;
char *filename = (argc > 6) ? argv[6]: 0;
if(0==strcmp(argv[2], "test")) predict_segmenter(data, cfg, weights, filename);
else if(0==strcmp(argv[2], "train")) train_segmenter(data, cfg, weights, gpus, ngpus, clear);
else if(0==strcmp(argv[2], "train")) train_segmenter(data, cfg, weights, gpus, ngpus, clear, display);
else if(0==strcmp(argv[2], "demo")) demo_segmenter(data, cfg, weights, cam_index, filename);
}

48
src/data.c
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@ -623,6 +623,54 @@ data load_data_seg(int n, char **paths, int m, int w, int h, int classes, int mi
d.X.cols = h*w*3;
d.y.rows = n;
d.y.cols = h*w*classes/div/div;
d.y.vals = calloc(d.X.rows, sizeof(float*));
for(i = 0; i < n; ++i){
image orig = load_image_color(random_paths[i], 0, 0);
augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h);
image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect);
int flip = rand()%2;
if(flip) flip_image(sized);
random_distort_image(sized, hue, saturation, exposure);
d.X.vals[i] = sized.data;
image mask = get_segmentation_image(random_paths[i], orig.w, orig.h, classes);
//image mask = make_image(orig.w, orig.h, classes+1);
image sized_m = rotate_crop_image(mask, a.rad, a.scale/div, a.w/div, a.h/div, a.dx/div, a.dy/div, a.aspect);
if(flip) flip_image(sized_m);
d.y.vals[i] = sized_m.data;
free_image(orig);
free_image(mask);
/*
image rgb = mask_to_rgb(sized_m, classes);
show_image(rgb, "part");
show_image(sized, "orig");
cvWaitKey(0);
free_image(rgb);
*/
}
free(random_paths);
return d;
}
data load_data_iseg(int n, char **paths, int m, int w, int h, int classes, int min, int max, float angle, float aspect, float hue, float saturation, float exposure, int div)
{
char **random_paths = get_random_paths(paths, n, m);
int i;
data d = {0};
d.shallow = 0;
d.X.rows = n;
d.X.vals = calloc(d.X.rows, sizeof(float*));
d.X.cols = h*w*3;
d.y.rows = n;
d.y.cols = h*w*classes/div/div;
d.y.vals = calloc(d.X.rows, sizeof(float*));

35
src/deconvolutional_layer.c
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@ -112,20 +112,20 @@ layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size
l.output_gpu = cuda_make_array(l.output, l.batch*l.out_h*l.out_w*n);
if(batch_normalize){
l.mean_gpu = cuda_make_array(l.mean, n);
l.variance_gpu = cuda_make_array(l.variance, n);
l.mean_gpu = cuda_make_array(0, n);
l.variance_gpu = cuda_make_array(0, n);
l.rolling_mean_gpu = cuda_make_array(l.mean, n);
l.rolling_variance_gpu = cuda_make_array(l.variance, n);
l.rolling_mean_gpu = cuda_make_array(0, n);
l.rolling_variance_gpu = cuda_make_array(0, n);
l.mean_delta_gpu = cuda_make_array(l.mean, n);
l.variance_delta_gpu = cuda_make_array(l.variance, n);
l.mean_delta_gpu = cuda_make_array(0, n);
l.variance_delta_gpu = cuda_make_array(0, n);
l.scales_gpu = cuda_make_array(l.scales, n);
l.scale_updates_gpu = cuda_make_array(l.scale_updates, n);
l.scales_gpu = cuda_make_array(0, n);
l.scale_updates_gpu = cuda_make_array(0, n);
l.x_gpu = cuda_make_array(l.output, l.batch*l.out_h*l.out_w*n);
l.x_norm_gpu = cuda_make_array(l.output, l.batch*l.out_h*l.out_w*n);
l.x_gpu = cuda_make_array(0, l.batch*l.out_h*l.out_w*n);
l.x_norm_gpu = cuda_make_array(0, l.batch*l.out_h*l.out_w*n);
}
}
#ifdef CUDNN
@ -144,6 +144,21 @@ layer make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size
return l;
}
void denormalize_deconvolutional_layer(layer l)
{
int i, j;
for(i = 0; i < l.n; ++i){
float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001);
for(j = 0; j < l.c*l.size*l.size; ++j){
l.weights[i*l.c*l.size*l.size + j] *= scale;
}
l.biases[i] -= l.rolling_mean[i] * scale;
l.scales[i] = 1;
l.rolling_mean[i] = 0;
l.rolling_variance[i] = 1;
}
}
void resize_deconvolutional_layer(layer *l, int h, int w)
{
l->h = h;

2
src/image.c
View File

@ -199,7 +199,7 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs,
float prob = probs[i][class];
if(prob > thresh){
int width = im.h * .012;
int width = im.h * .006;
if(0){
width = pow(prob, 1./2.)*10+1;

5
src/network.c
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@ -312,6 +312,11 @@ void set_batch_network(network *net, int b)
if(net->layers[i].type == CONVOLUTIONAL){
cudnn_convolutional_setup(net->layers + i);
}
if(net->layers[i].type == DECONVOLUTIONAL){
layer *l = net->layers + i;
cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, l->out_h, l->out_w);
cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1);
}
#endif
}
}

1
src/network_kernels.cu
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@ -383,7 +383,6 @@ float train_networks(network *nets, int n, data d, int interval)
float sum = 0;
for(i = 0; i < n; ++i){
nets[i].learning_rate *= n;
data p = get_data_part(d, i, n);
threads[i] = train_network_in_thread(nets[i], p, errors + i);
}

32
src/region_layer.c
View File

@ -152,13 +152,13 @@ void forward_region_layer(const layer l, network net)
for(n = 0; n < l.n; ++n){
int index = entry_index(l, b, n*l.w*l.h, 0);
activate_array(l.output + index, 2*l.w*l.h, LOGISTIC);
index = entry_index(l, b, n*l.w*l.h, 4);
index = entry_index(l, b, n*l.w*l.h, l.coords);
if(!l.background) activate_array(l.output + index, l.w*l.h, LOGISTIC);
}
}
if (l.softmax_tree){
int i;
int count = 5;
int count = l.coords + 1;
for (i = 0; i < l.softmax_tree->groups; ++i) {
int group_size = l.softmax_tree->group_size[i];
softmax_cpu(net.input + count, group_size, l.batch, l.inputs, l.n*l.w*l.h, 1, l.n*l.w*l.h, l.temperature, l.output + count);
@ -186,13 +186,13 @@ void forward_region_layer(const layer l, network net)
for(t = 0; t < 30; ++t){
box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1);
if(!truth.x) break;
int class = net.truth[t*(l.coords + 1) + b*l.truths + 4];
int class = net.truth[t*(l.coords + 1) + b*l.truths + l.coords];
float maxp = 0;
int maxi = 0;
if(truth.x > 100000 && truth.y > 100000){
for(n = 0; n < l.n*l.w*l.h; ++n){
int class_index = entry_index(l, b, n, 5);
int obj_index = entry_index(l, b, n, 4);
int class_index = entry_index(l, b, n, l.coords + 1);
int obj_index = entry_index(l, b, n, l.coords);
float scale = l.output[obj_index];
l.delta[obj_index] = l.noobject_scale * (0 - l.output[obj_index]);
float p = scale*get_hierarchy_probability(l.output + class_index, l.softmax_tree, class, l.w*l.h);
@ -201,8 +201,8 @@ void forward_region_layer(const layer l, network net)
maxi = n;
}
}
int class_index = entry_index(l, b, maxi, 5);
int obj_index = entry_index(l, b, maxi, 4);
int class_index = entry_index(l, b, maxi, l.coords + 1);
int obj_index = entry_index(l, b, maxi, l.coords);
delta_region_class(l.output, l.delta, class_index, class, l.classes, l.softmax_tree, l.class_scale, l.w*l.h, &avg_cat);
if(l.output[obj_index] < .3) l.delta[obj_index] = l.object_scale * (.3 - l.output[obj_index]);
else l.delta[obj_index] = 0;
@ -220,14 +220,14 @@ void forward_region_layer(const layer l, network net)
box pred = get_region_box(l.output, l.biases, n, box_index, i, j, l.w, l.h, l.w*l.h);
float best_iou = 0;
for(t = 0; t < 30; ++t){
box truth = float_to_box(net.truth + t*5 + b*l.truths, 1);
box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1);
if(!truth.x) break;
float iou = box_iou(pred, truth);
if (iou > best_iou) {
best_iou = iou;
}
}
int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4);
int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, l.coords);
avg_anyobj += l.output[obj_index];
l.delta[obj_index] = l.noobject_scale * (0 - l.output[obj_index]);
if(l.background) l.delta[obj_index] = l.noobject_scale * (1 - l.output[obj_index]);
@ -247,7 +247,7 @@ void forward_region_layer(const layer l, network net)
}
}
for(t = 0; t < 30; ++t){
box truth = float_to_box(net.truth + t*5 + b*l.truths, 1);
box truth = float_to_box(net.truth + t*(l.coords + 1) + b*l.truths, 1);
if(!truth.x) break;
float best_iou = 0;
@ -356,7 +356,7 @@ void get_region_boxes(layer l, int w, int h, int netw, int neth, float thresh, f
for (j = 0; j < l.h; ++j) {
for (i = 0; i < l.w/2; ++i) {
for (n = 0; n < l.n; ++n) {
for(z = 0; z < l.classes + 5; ++z){
for(z = 0; z < l.classes + l.coords + 1; ++z){
int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i;
int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1);
float swap = flip[i1];
@ -382,7 +382,7 @@ void get_region_boxes(layer l, int w, int h, int netw, int neth, float thresh, f
for(j = 0; j < l.classes; ++j){
probs[index][j] = 0;
}
int obj_index = entry_index(l, 0, n*l.w*l.h + i, 4);
int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords);
int box_index = entry_index(l, 0, n*l.w*l.h + i, 0);
float scale = l.background ? 1 : predictions[obj_index];
boxes[index] = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h, l.w*l.h);
@ -393,7 +393,7 @@ void get_region_boxes(layer l, int w, int h, int netw, int neth, float thresh, f
hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0, l.w*l.h);
if(map){
for(j = 0; j < 200; ++j){
int class_index = entry_index(l, 0, n*l.w*l.h + i, 5 + map[j]);
int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]);
float prob = scale*predictions[class_index];
probs[index][j] = (prob > thresh) ? prob : 0;
}
@ -405,7 +405,7 @@ void get_region_boxes(layer l, int w, int h, int netw, int neth, float thresh, f
} else {
float max = 0;
for(j = 0; j < l.classes; ++j){
int class_index = entry_index(l, 0, n*l.w*l.h + i, 5 + j);
int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + j);
float prob = scale*predictions[class_index];
probs[index][j] = (prob > thresh) ? prob : 0;
if(prob > max) max = prob;
@ -454,7 +454,7 @@ void forward_region_layer_gpu(const layer l, network net)
if (group_size > mmax) mmax = group_size;
}
printf("%d %d %d \n", l.softmax_tree->groups, mmin, mmax);
int index = entry_index(l, 0, 0, 5);
int index = entry_index(l, 0, 0, l.coords + 1);
softmax_tree(net.input_gpu + index, l.w*l.h, l.batch*l.n, l.inputs/l.n, 1, l.output_gpu + index, *l.softmax_tree);
/*
// TIMING CODE
@ -559,7 +559,7 @@ void zero_objectness(layer l)
int i, n;
for (i = 0; i < l.w*l.h; ++i){
for(n = 0; n < l.n; ++n){
int obj_index = entry_index(l, 0, n*l.w*l.h + i, 4);
int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords);
l.output[obj_index] = 0;
}
}