mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
captcha stuff
This commit is contained in:
Joseph Redmon
parent
0f645836f1
commit
5f4a5f59b0
@ -49,7 +49,7 @@ void forward_cost_layer(cost_layer layer, float *input, float *truth)
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if(layer.type == DETECTION){
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int i;
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for(i = 0; i < layer.batch*layer.inputs; ++i){
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if((i%5) && !truth[(i/5)*5]) layer.delta[i] = 0;
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if((i%25) && !truth[(i/25)*25]) layer.delta[i] = 0;
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}
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}
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*(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1);
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@ -71,7 +71,7 @@ void forward_cost_layer_gpu(cost_layer layer, float * input, float * truth)
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axpy_ongpu(layer.batch*layer.inputs, -1, input, 1, layer.delta_gpu, 1);
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if(layer.type==DETECTION){
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mask_ongpu(layer.inputs*layer.batch, layer.delta_gpu, truth, 5);
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mask_ongpu(layer.inputs*layer.batch, layer.delta_gpu, truth, 25);
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}
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cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*layer.inputs);
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199
src/darknet.c
199
src/darknet.c
@ -31,14 +31,17 @@ void test_parser()
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save_network(net, "cfg/trained_imagenet_smaller.cfg");
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}
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char *class_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
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#define AMNT 3
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void draw_detection(image im, float *box, int side)
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{
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int classes = 20;
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int elems = 4+classes+1;
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int j;
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int r, c;
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float amount[AMNT] = {0};
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for(r = 0; r < side*side; ++r){
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float val = box[r*5];
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float val = box[r*elems];
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for(j = 0; j < AMNT; ++j){
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if(val > amount[j]) {
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float swap = val;
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@ -51,21 +54,29 @@ void draw_detection(image im, float *box, int side)
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for(r = 0; r < side; ++r){
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for(c = 0; c < side; ++c){
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j = (r*side + c) * 5;
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printf("Prob: %f\n", box[j]);
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j = (r*side + c) * elems;
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//printf("%d\n", j);
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//printf("Prob: %f\n", box[j]);
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if(box[j] >= smallest){
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int class = max_index(box+j+1, classes);
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int z;
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for(z = 0; z < classes; ++z) printf("%f %s\n", box[j+1+z], class_names[z]);
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printf("%f %s\n", box[j+1+class], class_names[class]);
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float red = get_color(0,class,classes);
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float green = get_color(1,class,classes);
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float blue = get_color(2,class,classes);
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j += classes;
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int d = im.w/side;
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int y = r*d+box[j+1]*d;
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int x = c*d+box[j+2]*d;
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int h = box[j+3]*im.h;
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int w = box[j+4]*im.w;
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//printf("%f %f %f %f\n", box[j+1], box[j+2], box[j+3], box[j+4]);
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//printf("%d %d %d %d\n", x, y, w, h);
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//printf("%d %d %d %d\n", x-w/2, y-h/2, x+w/2, y+h/2);
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draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2);
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draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2,red,green,blue);
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}
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}
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}
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//printf("Done\n");
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show_image(im, "box");
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cvWaitKey(0);
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}
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@ -100,24 +111,24 @@ void train_detection_net(char *cfgfile, char *weightfile)
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srand(time(0));
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//srand(23410);
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int i = net.seen/imgs;
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list *plist = get_paths("/home/pjreddie/data/imagenet/horse_pos.txt");
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list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
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char **paths = (char **)list_to_array(plist);
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printf("%d\n", plist->size);
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data train, buffer;
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int im_dim = 512;
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int jitter = 64;
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pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, im_dim, im_dim, 7, 7, jitter, &buffer);
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pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, 20, im_dim, im_dim, 7, 7, jitter, &buffer);
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clock_t time;
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while(1){
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i += 1;
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time=clock();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data_detection_thread(imgs, paths, plist->size, im_dim, im_dim, 7, 7, jitter, &buffer);
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load_thread = load_data_detection_thread(imgs, paths, plist->size, 20, im_dim, im_dim, 7, 7, jitter, &buffer);
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/*
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image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[923]);
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draw_detection(im, train.y.vals[923], 7);
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/*
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image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[0]);
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draw_detection(im, train.y.vals[0], 7);
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show_image(im, "truth");
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cvWaitKey(0);
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*/
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@ -128,7 +139,7 @@ void train_detection_net(char *cfgfile, char *weightfile)
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net.seen += imgs;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
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if(i%100==0){
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if(i%800==0){
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char buff[256];
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sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
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save_weights(net, buff);
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@ -146,17 +157,20 @@ void validate_detection_net(char *cfgfile, char *weightfile)
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fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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srand(time(0));
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list *plist = get_paths("/home/pjreddie/data/imagenet/detection.val");
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list *plist = get_paths("/home/pjreddie/data/voc/val.txt");
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char **paths = (char **)list_to_array(plist);
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int num_output = 1225;
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int im_size = 448;
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int classes = 20;
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int m = plist->size;
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int i = 0;
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int splits = 50;
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int splits = 100;
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int num = (i+1)*m/splits - i*m/splits;
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fprintf(stderr, "%d\n", m);
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data val, buffer;
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pthread_t load_thread = load_data_thread(paths, num, 0, 0, 245, 224, 224, &buffer);
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pthread_t load_thread = load_data_thread(paths, num, 0, 0, num_output, im_size, im_size, &buffer);
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clock_t time;
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for(i = 1; i <= splits; ++i){
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time=clock();
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@ -165,23 +179,33 @@ void validate_detection_net(char *cfgfile, char *weightfile)
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num = (i+1)*m/splits - i*m/splits;
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char **part = paths+(i*m/splits);
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if(i != splits) load_thread = load_data_thread(part, num, 0, 0, 245, 224, 224, &buffer);
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if(i != splits) load_thread = load_data_thread(part, num, 0, 0, num_output, im_size, im_size, &buffer);
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fprintf(stderr, "Loaded: %lf seconds\n", sec(clock()-time));
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fprintf(stderr, "%d: Loaded: %lf seconds\n", i, sec(clock()-time));
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matrix pred = network_predict_data(net, val);
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int j, k;
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int j, k, class;
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for(j = 0; j < pred.rows; ++j){
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for(k = 0; k < pred.cols; k += 5){
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if (pred.vals[j][k] > .005){
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int index = k/5;
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for(k = 0; k < pred.cols; k += classes+4+1){
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/*
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int z;
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for(z = 0; z < 25; ++z) printf("%f, ", pred.vals[j][k+z]);
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printf("\n");
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*/
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float p = pred.vals[j][k];
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//if (pred.vals[j][k] > .001){
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for(class = 0; class < classes; ++class){
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int index = (k)/(classes+4+1);
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int r = index/7;
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int c = index%7;
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float y = (32.*(r + pred.vals[j][k+1]))/224.;
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float x = (32.*(c + pred.vals[j][k+2]))/224.;
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float h = (256.*(pred.vals[j][k+3]))/224.;
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float w = (256.*(pred.vals[j][k+4]))/224.;
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printf("%d %f %f %f %f %f\n", (i-1)*m/splits + j + 1, pred.vals[j][k], y, x, h, w);
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float y = (r + pred.vals[j][k+1+classes])/7.;
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float x = (c + pred.vals[j][k+2+classes])/7.;
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float h = pred.vals[j][k+3+classes];
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float w = pred.vals[j][k+4+classes];
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printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, p*pred.vals[j][k+class+1], y, x, h, w);
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}
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//}
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}
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}
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@ -191,8 +215,8 @@ void validate_detection_net(char *cfgfile, char *weightfile)
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}
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/*
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void train_imagenet_distributed(char *address)
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{
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void train_imagenet_distributed(char *address)
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{
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float avg_loss = 1;
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srand(time(0));
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network net = parse_network_cfg("cfg/net.cfg");
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@ -227,8 +251,8 @@ void train_imagenet_distributed(char *address)
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printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
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free_data(train);
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}
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}
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*/
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}
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*/
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void convert(char *cfgfile, char *outfile, char *weightfile)
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{
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@ -239,6 +263,111 @@ void convert(char *cfgfile, char *outfile, char *weightfile)
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save_network(net, outfile);
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}
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void train_captcha(char *cfgfile, char *weightfile)
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{
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float avg_loss = -1;
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srand(time(0));
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char *base = basename(cfgfile);
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printf("%s\n", base);
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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int imgs = 1024;
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int i = net.seen/imgs;
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list *plist = get_paths("/data/captcha/train.list");
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char **paths = (char **)list_to_array(plist);
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printf("%d\n", plist->size);
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clock_t time;
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while(1){
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++i;
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time=clock();
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data train = load_data_captcha(paths, imgs, plist->size, 10, 60, 200);
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translate_data_rows(train, -128);
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scale_data_rows(train, 1./128);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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float loss = train_network(net, train);
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net.seen += imgs;
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if(avg_loss == -1) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen);
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free_data(train);
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if(i%100==0){
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char buff[256];
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sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
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save_weights(net, buff);
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}
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}
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}
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void validate_captcha(char *cfgfile, char *weightfile)
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{
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srand(time(0));
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char *base = basename(cfgfile);
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printf("%s\n", base);
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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int imgs = 1000;
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int numchars = 37;
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list *plist = get_paths("/data/captcha/valid.list");
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char **paths = (char **)list_to_array(plist);
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data valid = load_data_captcha(paths, imgs, 0, 10, 60, 200);
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translate_data_rows(valid, -128);
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scale_data_rows(valid, 1./128);
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matrix pred = network_predict_data(net, valid);
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int i, k;
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int correct = 0;
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int total = 0;
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int accuracy = 0;
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for(i = 0; i < imgs; ++i){
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int allcorrect = 1;
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for(k = 0; k < 10; ++k){
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char truth = int_to_alphanum(max_index(valid.y.vals[i]+k*numchars, numchars));
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char prediction = int_to_alphanum(max_index(pred.vals[i]+k*numchars, numchars));
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if (truth != prediction) allcorrect=0;
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if (truth != '.' && truth == prediction) ++correct;
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if (truth != '.' || truth != prediction) ++total;
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}
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accuracy += allcorrect;
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}
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printf("Word Accuracy: %f, Char Accuracy %f\n", (float)accuracy/imgs, (float)correct/total);
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free_data(valid);
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}
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void test_captcha(char *cfgfile, char *weightfile)
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{
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srand(time(0));
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char *base = basename(cfgfile);
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printf("%s\n", base);
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network net = parse_network_cfg(cfgfile);
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set_batch_network(&net, 1);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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clock_t time;
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char filename[256];
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while(1){
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printf("Enter filename: ");
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fgets(filename, 256, stdin);
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strtok(filename, "\n");
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time = clock();
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image im = load_image_color(filename, 60, 200);
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translate_image(im, -128);
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scale_image(im, 1/128.);
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float *X = im.data;
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time=clock();
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float *predictions = network_predict(net, X);
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printf("Predicted in %f\n", sec(clock() - time));
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print_letters(predictions, 10);
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free_image(im);
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}
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}
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void train_imagenet(char *cfgfile, char *weightfile)
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{
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float avg_loss = -1;
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@ -333,6 +462,7 @@ void test_detection(char *cfgfile, char *weightfile)
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if(weightfile){
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load_weights(&net, weightfile);
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}
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int im_size = 224;
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set_batch_network(&net, 1);
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srand(2222222);
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clock_t time;
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@ -340,7 +470,7 @@ void test_detection(char *cfgfile, char *weightfile)
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while(1){
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fgets(filename, 256, stdin);
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strtok(filename, "\n");
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image im = load_image_color(filename, 224, 224);
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image im = load_image_color(filename, im_size, im_size);
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translate_image(im, -128);
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scale_image(im, 1/128.);
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printf("%d %d %d\n", im.h, im.w, im.c);
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@ -814,6 +944,9 @@ int main(int argc, char **argv)
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else if(0==strcmp(argv[1], "nist")) train_nist(argv[2]);
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else if(0==strcmp(argv[1], "ctest")) test_cifar10(argv[2]);
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else if(0==strcmp(argv[1], "train")) train_imagenet(argv[2], (argc > 3)? argv[3] : 0);
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else if(0==strcmp(argv[1], "captcha")) train_captcha(argv[2], (argc > 3)? argv[3] : 0);
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else if(0==strcmp(argv[1], "tcaptcha")) test_captcha(argv[2], (argc > 3)? argv[3] : 0);
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else if(0==strcmp(argv[1], "vcaptcha")) validate_captcha(argv[2], (argc > 3)? argv[3] : 0);
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else if(0==strcmp(argv[1], "testseg")) test_voc_segment(argv[2], (argc > 3)? argv[3] : 0);
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//else if(0==strcmp(argv[1], "client")) train_imagenet_distributed(argv[2]);
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else if(0==strcmp(argv[1], "detect")) test_detection(argv[2], (argc > 3)? argv[3] : 0);
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183
src/data.c
183
src/data.c
@ -17,6 +17,7 @@ struct load_args{
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int nh;
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int nw;
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int jitter;
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int classes;
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data *d;
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};
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@ -33,53 +34,16 @@ list *get_paths(char *filename)
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return lines;
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}
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void fill_truth_detection(char *path, float *truth, int height, int width, int num_height, int num_width, int dy, int dx, int jitter)
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{
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int box_height = height/num_height;
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int box_width = width/num_width;
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char *labelpath = find_replace(path, "imgs", "det/train");
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labelpath = find_replace(labelpath, ".JPEG", ".txt");
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FILE *file = fopen(labelpath, "r");
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if(!file) file_error(labelpath);
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float x, y, h, w;
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while(fscanf(file, "%f %f %f %f", &x, &y, &w, &h) == 4){
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x *= width + jitter;
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y *= height + jitter;
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x -= dx;
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y -= dy;
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int i = x/box_width;
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int j = y/box_height;
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if(i < 0) i = 0;
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if(i >= num_width) i = num_width-1;
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if(j < 0) j = 0;
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if(j >= num_height) j = num_height-1;
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float dw = (x - i*box_width)/box_width;
|
||||
float dh = (y - j*box_height)/box_height;
|
||||
//printf("%d %d %f %f\n", i, j, dh, dw);
|
||||
int index = (i+j*num_width)*5;
|
||||
truth[index++] = 1;
|
||||
truth[index++] = dh;
|
||||
truth[index++] = dw;
|
||||
truth[index++] = h*(height+jitter)/height;
|
||||
truth[index++] = w*(width+jitter)/width;
|
||||
}
|
||||
fclose(file);
|
||||
}
|
||||
|
||||
void fill_truth(char *path, char **labels, int k, float *truth)
|
||||
char **get_random_paths(char **paths, int n, int m)
|
||||
{
|
||||
char **random_paths = calloc(n, sizeof(char*));
|
||||
int i;
|
||||
memset(truth, 0, k*sizeof(float));
|
||||
int count = 0;
|
||||
for(i = 0; i < k; ++i){
|
||||
if(strstr(path, labels[i])){
|
||||
truth[i] = 1;
|
||||
++count;
|
||||
for(i = 0; i < n; ++i){
|
||||
int index = rand()%m;
|
||||
random_paths[i] = paths[index];
|
||||
if(i == 0) printf("%s\n", paths[index]);
|
||||
}
|
||||
}
|
||||
if(count != 1) printf("%d, %s\n", count, path);
|
||||
return random_paths;
|
||||
}
|
||||
|
||||
matrix load_image_paths(char **paths, int n, int h, int w)
|
||||
@ -98,16 +62,100 @@ matrix load_image_paths(char **paths, int n, int h, int w)
|
||||
return X;
|
||||
}
|
||||
|
||||
char **get_random_paths(char **paths, int n, int m)
|
||||
void fill_truth_detection(char *path, float *truth, int classes, int height, int width, int num_height, int num_width, int dy, int dx, int jitter, int flip)
|
||||
{
|
||||
int box_height = height/num_height;
|
||||
int box_width = width/num_width;
|
||||
char *labelpath = find_replace(path, "VOC2012/JPEGImages", "labels");
|
||||
labelpath = find_replace(labelpath, ".jpg", ".txt");
|
||||
FILE *file = fopen(labelpath, "r");
|
||||
if(!file) file_error(labelpath);
|
||||
float x, y, h, w;
|
||||
int id;
|
||||
while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){
|
||||
if(flip) x = 1-x;
|
||||
x *= width + jitter;
|
||||
y *= height + jitter;
|
||||
x -= dx;
|
||||
y -= dy;
|
||||
int i = x/box_width;
|
||||
int j = y/box_height;
|
||||
|
||||
if(i < 0) i = 0;
|
||||
if(i >= num_width) i = num_width-1;
|
||||
if(j < 0) j = 0;
|
||||
if(j >= num_height) j = num_height-1;
|
||||
|
||||
float dw = (x - i*box_width)/box_width;
|
||||
float dh = (y - j*box_height)/box_height;
|
||||
//printf("%d %d %d %f %f\n", id, i, j, dh, dw);
|
||||
int index = (i+j*num_width)*(4+classes+1);
|
||||
truth[index++] = 1;
|
||||
truth[index+id] = 1;
|
||||
index += classes;
|
||||
truth[index++] = dh;
|
||||
truth[index++] = dw;
|
||||
truth[index++] = h*(height+jitter)/height;
|
||||
truth[index++] = w*(width+jitter)/width;
|
||||
}
|
||||
fclose(file);
|
||||
}
|
||||
|
||||
#define NUMCHARS 37
|
||||
|
||||
void print_letters(float *pred, int n)
|
||||
{
|
||||
char **random_paths = calloc(n, sizeof(char*));
|
||||
int i;
|
||||
for(i = 0; i < n; ++i){
|
||||
int index = rand()%m;
|
||||
random_paths[i] = paths[index];
|
||||
if(i == 0) printf("%s\n", paths[index]);
|
||||
int index = max_index(pred+i*NUMCHARS, NUMCHARS);
|
||||
printf("%c", int_to_alphanum(index));
|
||||
}
|
||||
return random_paths;
|
||||
printf("\n");
|
||||
}
|
||||
|
||||
void fill_truth_captcha(char *path, int n, float *truth)
|
||||
{
|
||||
char *begin = strrchr(path, '/');
|
||||
++begin;
|
||||
int i;
|
||||
for(i = 0; i < strlen(begin) && i < n && begin[i] != '.'; ++i){
|
||||
int index = alphanum_to_int(begin[i]);
|
||||
if(index > 35) printf("Bad %c\n", begin[i]);
|
||||
truth[i*NUMCHARS+index] = 1;
|
||||
}
|
||||
for(;i < n; ++i){
|
||||
truth[i*NUMCHARS + NUMCHARS-1] = 1;
|
||||
}
|
||||
}
|
||||
|
||||
data load_data_captcha(char **paths, int n, int m, int k, int h, int w)
|
||||
{
|
||||
if(m) paths = get_random_paths(paths, n, m);
|
||||
data d;
|
||||
d.shallow = 0;
|
||||
d.X = load_image_paths(paths, n, h, w);
|
||||
d.y = make_matrix(n, k*NUMCHARS);
|
||||
int i;
|
||||
for(i = 0; i < n; ++i){
|
||||
fill_truth_captcha(paths[i], k, d.y.vals[i]);
|
||||
}
|
||||
if(m) free(paths);
|
||||
return d;
|
||||
}
|
||||
|
||||
|
||||
void fill_truth(char *path, char **labels, int k, float *truth)
|
||||
{
|
||||
int i;
|
||||
memset(truth, 0, k*sizeof(float));
|
||||
int count = 0;
|
||||
for(i = 0; i < k; ++i){
|
||||
if(strstr(path, labels[i])){
|
||||
truth[i] = 1;
|
||||
++count;
|
||||
}
|
||||
}
|
||||
if(count != 1) printf("%d, %s\n", count, path);
|
||||
}
|
||||
|
||||
matrix load_labels_paths(char **paths, int n, char **labels, int k)
|
||||
@ -120,17 +168,6 @@ matrix load_labels_paths(char **paths, int n, char **labels, int k)
|
||||
return y;
|
||||
}
|
||||
|
||||
matrix load_labels_detection(char **paths, int n, int height, int width, int num_height, int num_width)
|
||||
{
|
||||
int k = num_height*num_width*5;
|
||||
matrix y = make_matrix(n, k);
|
||||
int i;
|
||||
for(i = 0; i < n; ++i){
|
||||
fill_truth_detection(paths[i], y.vals[i], height, width, num_height, num_width, 0, 0, 0);
|
||||
}
|
||||
return y;
|
||||
}
|
||||
|
||||
data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w)
|
||||
{
|
||||
list *plist = get_paths(filename);
|
||||
@ -165,20 +202,22 @@ void free_data(data d)
|
||||
}
|
||||
}
|
||||
|
||||
data load_data_detection_jitter_random(int n, char **paths, int m, int h, int w, int nh, int nw, int jitter)
|
||||
data load_data_detection_jitter_random(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter)
|
||||
{
|
||||
char **random_paths = get_random_paths(paths, n, m);
|
||||
int i;
|
||||
data d;
|
||||
d.shallow = 0;
|
||||
d.X = load_image_paths(random_paths, n, h, w);
|
||||
int k = nh*nw*5;
|
||||
int k = nh*nw*(4+classes+1);
|
||||
d.y = make_matrix(n, k);
|
||||
for(i = 0; i < n; ++i){
|
||||
int dx = rand()%jitter;
|
||||
int dy = rand()%jitter;
|
||||
fill_truth_detection(random_paths[i], d.y.vals[i], h-jitter, w-jitter, nh, nw, dy, dx, jitter);
|
||||
int flip = rand()%2;
|
||||
fill_truth_detection(random_paths[i], d.y.vals[i], classes, h-jitter, w-jitter, nh, nw, dy, dx, jitter, flip);
|
||||
image a = float_to_image(h, w, 3, d.X.vals[i]);
|
||||
if(flip) flip_image(a);
|
||||
jitter_image(a,h-jitter,w-jitter,dy,dx);
|
||||
}
|
||||
d.X.cols = (h-jitter)*(w-jitter)*3;
|
||||
@ -189,14 +228,14 @@ data load_data_detection_jitter_random(int n, char **paths, int m, int h, int w,
|
||||
void *load_detection_thread(void *ptr)
|
||||
{
|
||||
struct load_args a = *(struct load_args*)ptr;
|
||||
*a.d = load_data_detection_jitter_random(a.n, a.paths, a.m, a.h, a.w, a.nh, a.nw, a.jitter);
|
||||
*a.d = load_data_detection_jitter_random(a.n, a.paths, a.m, a.classes, a.h, a.w, a.nh, a.nw, a.jitter);
|
||||
translate_data_rows(*a.d, -128);
|
||||
scale_data_rows(*a.d, 1./128);
|
||||
free(ptr);
|
||||
return 0;
|
||||
}
|
||||
|
||||
pthread_t load_data_detection_thread(int n, char **paths, int m, int h, int w, int nh, int nw, int jitter, data *d)
|
||||
pthread_t load_data_detection_thread(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter, data *d)
|
||||
{
|
||||
pthread_t thread;
|
||||
struct load_args *args = calloc(1, sizeof(struct load_args));
|
||||
@ -207,6 +246,7 @@ pthread_t load_data_detection_thread(int n, char **paths, int m, int h, int w, i
|
||||
args->w = w;
|
||||
args->nh = nh;
|
||||
args->nw = nw;
|
||||
args->classes = classes;
|
||||
args->jitter = jitter;
|
||||
args->d = d;
|
||||
if(pthread_create(&thread, 0, load_detection_thread, args)) {
|
||||
@ -215,17 +255,6 @@ pthread_t load_data_detection_thread(int n, char **paths, int m, int h, int w, i
|
||||
return thread;
|
||||
}
|
||||
|
||||
data load_data_detection_random(int n, char **paths, int m, int h, int w, int nh, int nw)
|
||||
{
|
||||
char **random_paths = get_random_paths(paths, n, m);
|
||||
data d;
|
||||
d.shallow = 0;
|
||||
d.X = load_image_paths(random_paths, n, h, w);
|
||||
d.y = load_labels_detection(random_paths, n, h, w, nh, nw);
|
||||
free(random_paths);
|
||||
return d;
|
||||
}
|
||||
|
||||
data load_data(char **paths, int n, int m, char **labels, int k, int h, int w)
|
||||
{
|
||||
if(m) paths = get_random_paths(paths, n, m);
|
||||
|
||||
@ -14,12 +14,13 @@ typedef struct{
|
||||
|
||||
void free_data(data d);
|
||||
|
||||
void print_letters(float *pred, int n);
|
||||
data load_data_captcha(char **paths, int n, int m, int k, int h, int w);
|
||||
data load_data(char **paths, int n, int m, char **labels, int k, int h, int w);
|
||||
pthread_t load_data_thread(char **paths, int n, int m, char **labels, int k, int h, int w, data *d);
|
||||
|
||||
pthread_t load_data_detection_thread(int n, char **paths, int m, int h, int w, int nh, int nw, int jitter, data *d);
|
||||
data load_data_detection_jitter_random(int n, char **paths, int m, int h, int w, int nh, int nw, int jitter);
|
||||
data load_data_detection_random(int n, char **paths, int m, int h, int w, int nh, int nw);
|
||||
pthread_t load_data_detection_thread(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter, data *d);
|
||||
data load_data_detection_jitter_random(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter);
|
||||
|
||||
data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w);
|
||||
data load_cifar10_data(char *filename);
|
||||
|
||||
58
src/image.c
58
src/image.c
@ -4,9 +4,23 @@
|
||||
|
||||
int windows = 0;
|
||||
|
||||
void draw_box(image a, int x1, int y1, int x2, int y2)
|
||||
float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} };
|
||||
|
||||
float get_color(int c, int x, int max)
|
||||
{
|
||||
int i, c;
|
||||
float ratio = ((float)x/max)*5;
|
||||
int i = floor(ratio);
|
||||
int j = ceil(ratio);
|
||||
ratio -= i;
|
||||
float r = (1-ratio) * colors[i][c] + ratio*colors[j][c];
|
||||
printf("%f\n", r);
|
||||
return r;
|
||||
}
|
||||
|
||||
void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b)
|
||||
{
|
||||
normalize_image(a);
|
||||
int i;
|
||||
if(x1 < 0) x1 = 0;
|
||||
if(x1 >= a.w) x1 = a.w-1;
|
||||
if(x2 < 0) x2 = 0;
|
||||
@ -17,17 +31,25 @@ void draw_box(image a, int x1, int y1, int x2, int y2)
|
||||
if(y2 < 0) y2 = 0;
|
||||
if(y2 >= a.h) y2 = a.h-1;
|
||||
|
||||
for(c = 0; c < a.c; ++c){
|
||||
for(i = x1; i < x2; ++i){
|
||||
a.data[i + y1*a.w + c*a.w*a.h] = (c==0)?1:-1;
|
||||
a.data[i + y2*a.w + c*a.w*a.h] = (c==0)?1:-1;
|
||||
a.data[i + y1*a.w + 0*a.w*a.h] = b;
|
||||
a.data[i + y2*a.w + 0*a.w*a.h] = b;
|
||||
|
||||
a.data[i + y1*a.w + 1*a.w*a.h] = g;
|
||||
a.data[i + y2*a.w + 1*a.w*a.h] = g;
|
||||
|
||||
a.data[i + y1*a.w + 2*a.w*a.h] = r;
|
||||
a.data[i + y2*a.w + 2*a.w*a.h] = r;
|
||||
}
|
||||
}
|
||||
for(c = 0; c < a.c; ++c){
|
||||
for(i = y1; i < y2; ++i){
|
||||
a.data[x1 + i*a.w + c*a.w*a.h] = (c==0)?1:-1;
|
||||
a.data[x2 + i*a.w + c*a.w*a.h] = (c==0)?1:-1;
|
||||
}
|
||||
a.data[x1 + i*a.w + 0*a.w*a.h] = b;
|
||||
a.data[x2 + i*a.w + 0*a.w*a.h] = b;
|
||||
|
||||
a.data[x1 + i*a.w + 1*a.w*a.h] = g;
|
||||
a.data[x2 + i*a.w + 1*a.w*a.h] = g;
|
||||
|
||||
a.data[x1 + i*a.w + 2*a.w*a.h] = r;
|
||||
a.data[x2 + i*a.w + 2*a.w*a.h] = r;
|
||||
}
|
||||
}
|
||||
|
||||
@ -46,6 +68,22 @@ void jitter_image(image a, int h, int w, int dh, int dw)
|
||||
}
|
||||
}
|
||||
|
||||
void flip_image(image a)
|
||||
{
|
||||
int i,j,k;
|
||||
for(k = 0; k < a.c; ++k){
|
||||
for(i = 0; i < a.h; ++i){
|
||||
for(j = 0; j < a.w/2; ++j){
|
||||
int index = j + a.w*(i + a.h*(k));
|
||||
int flip = (a.w - j - 1) + a.w*(i + a.h*(k));
|
||||
float swap = a.data[flip];
|
||||
a.data[flip] = a.data[index];
|
||||
a.data[index] = swap;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
image image_distance(image a, image b)
|
||||
{
|
||||
int i,j;
|
||||
|
||||
@ -11,8 +11,10 @@ typedef struct {
|
||||
float *data;
|
||||
} image;
|
||||
|
||||
float get_color(int c, int x, int max);
|
||||
void jitter_image(image a, int h, int w, int dh, int dw);
|
||||
void draw_box(image a, int x1, int y1, int x2, int y2);
|
||||
void flip_image(image a);
|
||||
void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b);
|
||||
image image_distance(image a, image b);
|
||||
void scale_image(image m, float s);
|
||||
void translate_image(image m, float s);
|
||||
|
||||
@ -21,6 +21,7 @@ extern "C" {
|
||||
|
||||
extern "C" float * get_network_output_gpu_layer(network net, int i);
|
||||
extern "C" float * get_network_delta_gpu_layer(network net, int i);
|
||||
float *get_network_output_gpu(network net);
|
||||
|
||||
void forward_network_gpu(network net, float * input, float * truth, int train)
|
||||
{
|
||||
@ -219,6 +220,10 @@ float train_network_datum_gpu(network net, float *x, float *y)
|
||||
//time = clock();
|
||||
update_network_gpu(net);
|
||||
float error = get_network_cost(net);
|
||||
|
||||
//print_letters(y, 50);
|
||||
//float *out = get_network_output_gpu(net);
|
||||
//print_letters(out, 50);
|
||||
//printf("updt %f\n", sec(clock() - time));
|
||||
//time = clock();
|
||||
return error;
|
||||
|
||||
@ -191,6 +191,7 @@ connected_layer *parse_connected(list *options, network *net, int count)
|
||||
softmax_layer *parse_softmax(list *options, network *net, int count)
|
||||
{
|
||||
int input;
|
||||
int groups = option_find_int(options, "groups",1);
|
||||
if(count == 0){
|
||||
input = option_find_int(options, "input",1);
|
||||
net->batch = option_find_int(options, "batch",1);
|
||||
@ -198,7 +199,7 @@ softmax_layer *parse_softmax(list *options, network *net, int count)
|
||||
}else{
|
||||
input = get_network_output_size_layer(*net, count-1);
|
||||
}
|
||||
softmax_layer *layer = make_softmax_layer(net->batch, input);
|
||||
softmax_layer *layer = make_softmax_layer(net->batch, groups, input);
|
||||
option_unused(options);
|
||||
return layer;
|
||||
}
|
||||
|
||||
@ -5,16 +5,18 @@
|
||||
#include <math.h>
|
||||
#include <stdlib.h>
|
||||
#include <stdio.h>
|
||||
#include <assert.h>
|
||||
|
||||
softmax_layer *make_softmax_layer(int batch, int inputs)
|
||||
softmax_layer *make_softmax_layer(int batch, int groups, int inputs)
|
||||
{
|
||||
assert(inputs%groups == 0);
|
||||
fprintf(stderr, "Softmax Layer: %d inputs\n", inputs);
|
||||
softmax_layer *layer = calloc(1, sizeof(softmax_layer));
|
||||
layer->batch = batch;
|
||||
layer->groups = groups;
|
||||
layer->inputs = inputs;
|
||||
layer->output = calloc(inputs*batch, sizeof(float));
|
||||
layer->delta = calloc(inputs*batch, sizeof(float));
|
||||
layer->jacobian = calloc(inputs*inputs*batch, sizeof(float));
|
||||
#ifdef GPU
|
||||
layer->output_gpu = cuda_make_array(layer->output, inputs*batch);
|
||||
layer->delta_gpu = cuda_make_array(layer->delta, inputs*batch);
|
||||
@ -22,23 +24,31 @@ softmax_layer *make_softmax_layer(int batch, int inputs)
|
||||
return layer;
|
||||
}
|
||||
|
||||
void forward_softmax_layer(const softmax_layer layer, float *input)
|
||||
void softmax_array(float *input, int n, float *output)
|
||||
{
|
||||
int i,b;
|
||||
for(b = 0; b < layer.batch; ++b){
|
||||
int i;
|
||||
float sum = 0;
|
||||
float largest = -FLT_MAX;
|
||||
for(i = 0; i < layer.inputs; ++i){
|
||||
if(input[i+b*layer.inputs] > largest) largest = input[i+b*layer.inputs];
|
||||
for(i = 0; i < n; ++i){
|
||||
if(input[i] > largest) largest = input[i];
|
||||
}
|
||||
for(i = 0; i < layer.inputs; ++i){
|
||||
sum += exp(input[i+b*layer.inputs]-largest);
|
||||
for(i = 0; i < n; ++i){
|
||||
sum += exp(input[i]-largest);
|
||||
}
|
||||
if(sum) sum = largest+log(sum);
|
||||
else sum = largest-100;
|
||||
for(i = 0; i < layer.inputs; ++i){
|
||||
layer.output[i+b*layer.inputs] = exp(input[i+b*layer.inputs]-sum);
|
||||
for(i = 0; i < n; ++i){
|
||||
output[i] = exp(input[i]-sum);
|
||||
}
|
||||
}
|
||||
|
||||
void forward_softmax_layer(const softmax_layer layer, float *input)
|
||||
{
|
||||
int b;
|
||||
int inputs = layer.inputs / layer.groups;
|
||||
int batch = layer.batch * layer.groups;
|
||||
for(b = 0; b < batch; ++b){
|
||||
softmax_array(input+b*inputs, inputs, layer.output+b*inputs);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@ -4,16 +4,16 @@
|
||||
typedef struct {
|
||||
int inputs;
|
||||
int batch;
|
||||
int groups;
|
||||
float *delta;
|
||||
float *output;
|
||||
float *jacobian;
|
||||
#ifdef GPU
|
||||
float * delta_gpu;
|
||||
float * output_gpu;
|
||||
#endif
|
||||
} softmax_layer;
|
||||
|
||||
softmax_layer *make_softmax_layer(int batch, int inputs);
|
||||
softmax_layer *make_softmax_layer(int batch, int groups, int inputs);
|
||||
void forward_softmax_layer(const softmax_layer layer, float *input);
|
||||
void backward_softmax_layer(const softmax_layer layer, float *delta);
|
||||
|
||||
|
||||
@ -34,7 +34,9 @@ extern "C" void pull_softmax_layer_output(const softmax_layer layer)
|
||||
|
||||
extern "C" void forward_softmax_layer_gpu(const softmax_layer layer, float *input)
|
||||
{
|
||||
forward_softmax_layer_kernel<<<cuda_gridsize(layer.batch), BLOCK>>>(layer.inputs, layer.batch, input, layer.output_gpu);
|
||||
int inputs = layer.inputs / layer.groups;
|
||||
int batch = layer.batch * layer.groups;
|
||||
forward_softmax_layer_kernel<<<cuda_gridsize(batch), BLOCK>>>(inputs, batch, input, layer.output_gpu);
|
||||
check_error(cudaPeekAtLastError());
|
||||
|
||||
/*
|
||||
|
||||
11
src/utils.c
11
src/utils.c
@ -8,6 +8,17 @@
|
||||
|
||||
#include "utils.h"
|
||||
|
||||
|
||||
int alphanum_to_int(char c)
|
||||
{
|
||||
return (c < 58) ? c - 48 : c-87;
|
||||
}
|
||||
char int_to_alphanum(int i)
|
||||
{
|
||||
if (i == 36) return '.';
|
||||
return (i < 10) ? i + 48 : i + 87;
|
||||
}
|
||||
|
||||
void pm(int M, int N, float *A)
|
||||
{
|
||||
int i,j;
|
||||
|
||||
@ -4,6 +4,8 @@
|
||||
#include <time.h>
|
||||
#include "list.h"
|
||||
|
||||
int alphanum_to_int(char c);
|
||||
char int_to_alphanum(int i);
|
||||
void read_all(int fd, char *buffer, size_t bytes);
|
||||
void write_all(int fd, char *buffer, size_t bytes);
|
||||
char *find_replace(char *str, char *orig, char *rep);
|
||||
|
||||
Loading…
Reference in New Issue
Block a user