mirror of
https://github.com/pjreddie/darknet.git
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yolo working w/ regions
This commit is contained in:
Joseph Redmon
parent
393dc8eb6f
commit
c53e03348c
2
Makefile
2
Makefile
@ -34,7 +34,7 @@ CFLAGS+= -DGPU
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LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
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endif
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OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o region_layer.o layer.o compare.o yoloplus.o
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OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o region_layer.o layer.o compare.o swag.o
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ifeq ($(GPU), 1)
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OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
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endif
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@ -1,15 +1,17 @@
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[net]
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batch=128
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batch=256
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subdivisions=1
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height=256
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width=256
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channels=3
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momentum=0.9
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decay=0.0005
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learning_rate=0.01
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policy=poly
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power=.5
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max_batches=600000
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policy=step
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scale=.1
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step=100000
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max_batches=400000
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[crop]
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crop_height=224
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14
src/coco.c
14
src/coco.c
@ -111,20 +111,6 @@ void train_coco(char *cfgfile, char *weightfile)
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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-1)*imgs <= N && i*imgs > N){
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fprintf(stderr, "First stage done\n");
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net.learning_rate *= 10;
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char buff[256];
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sprintf(buff, "%s/%s_first_stage.weights", backup_directory, base);
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save_weights(net, buff);
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}
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if((i-1)*imgs <= 80*N && i*imgs > N*80){
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fprintf(stderr, "Second stage done.\n");
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char buff[256];
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sprintf(buff, "%s/%s_second_stage.weights", backup_directory, base);
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save_weights(net, buff);
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}
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if(i%1000==0){
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
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@ -175,8 +175,8 @@ int bbox_comparator(const void *a, const void *b)
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image im1 = load_image_color(box1.filename, net.w, net.h);
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image im2 = load_image_color(box2.filename, net.w, net.h);
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float *X = calloc(net.w*net.h*net.c, sizeof(float));
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memcpy(X, im1.data, im1.w*im1.h*im1.c);
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memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c);
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memcpy(X, im1.data, im1.w*im1.h*im1.c*sizeof(float));
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memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float));
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float *predictions = network_predict(net, X);
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free_image(im1);
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@ -13,7 +13,7 @@
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extern void run_imagenet(int argc, char **argv);
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extern void run_yolo(int argc, char **argv);
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extern void run_yoloplus(int argc, char **argv);
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extern void run_swag(int argc, char **argv);
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extern void run_coco(int argc, char **argv);
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extern void run_writing(int argc, char **argv);
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extern void run_captcha(int argc, char **argv);
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@ -179,8 +179,8 @@ int main(int argc, char **argv)
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average(argc, argv);
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} else if (0 == strcmp(argv[1], "yolo")){
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run_yolo(argc, argv);
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} else if (0 == strcmp(argv[1], "yoloplus")){
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run_yoloplus(argc, argv);
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} else if (0 == strcmp(argv[1], "swag")){
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run_swag(argc, argv);
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} else if (0 == strcmp(argv[1], "coco")){
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run_coco(argc, argv);
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} else if (0 == strcmp(argv[1], "compare")){
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@ -176,8 +176,10 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int
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int index = (col+row*num_boxes)*(5+classes);
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if (truth[index]) continue;
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truth[index++] = 1;
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if (classes) truth[index+id] = 1;
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if (id < classes) truth[index+id] = 1;
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index += classes;
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truth[index++] = x;
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truth[index++] = y;
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truth[index++] = w;
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@ -30,6 +30,7 @@ typedef struct {
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int batch;
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int inputs;
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int outputs;
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int truths;
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int h,w,c;
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int out_h, out_w, out_c;
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int n;
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@ -40,10 +41,12 @@ typedef struct {
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int pad;
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int crop_width;
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int crop_height;
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int sqrt;
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int flip;
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float angle;
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float saturation;
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float exposure;
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int softmax;
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int classes;
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int coords;
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int background;
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@ -48,7 +48,7 @@ float get_current_rate(network net)
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case POLY:
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return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
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case SIG:
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return net.learning_rate * (1/(1+exp(net.gamma*(batch_num - net.step))));
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return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step))));
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default:
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fprintf(stderr, "Policy is weird!\n");
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return net.learning_rate;
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@ -134,6 +134,7 @@ float train_network_datum_gpu(network net, float *x, float *y)
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network_state state;
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int x_size = get_network_input_size(net)*net.batch;
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int y_size = get_network_output_size(net)*net.batch;
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if(net.layers[net.n-1].type == REGION) y_size = net.layers[net.n-1].truths*net.batch;
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if(!*net.input_gpu){
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*net.input_gpu = cuda_make_array(x, x_size);
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*net.truth_gpu = cuda_make_array(y, y_size);
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@ -182,6 +182,10 @@ region_layer parse_region(list *options, size_params params)
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int num = option_find_int(options, "num", 1);
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int side = option_find_int(options, "side", 7);
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region_layer layer = make_region_layer(params.batch, params.inputs, num, side, classes, coords, rescore);
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int softmax = option_find_int(options, "softmax", 0);
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int sqrt = option_find_int(options, "sqrt", 0);
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layer.softmax = softmax;
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layer.sqrt = sqrt;
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return layer;
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}
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@ -22,15 +22,15 @@ region_layer make_region_layer(int batch, int inputs, int n, int side, int class
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l.coords = coords;
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l.rescore = rescore;
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l.side = side;
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assert(side*side*l.coords*l.n == inputs);
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assert(side*side*((1 + l.coords)*l.n + l.classes) == inputs);
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l.cost = calloc(1, sizeof(float));
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int outputs = l.n*5*side*side;
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l.outputs = outputs;
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l.output = calloc(batch*outputs, sizeof(float));
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l.delta = calloc(batch*inputs, sizeof(float));
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#ifdef GPU
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l.output_gpu = cuda_make_array(l.output, batch*outputs);
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l.delta_gpu = cuda_make_array(l.delta, batch*inputs);
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l.outputs = l.inputs;
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l.truths = l.side*l.side*(1+l.coords+l.classes);
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l.output = calloc(batch*l.outputs, sizeof(float));
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l.delta = calloc(batch*l.outputs, sizeof(float));
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#ifdef GPU
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l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
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l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
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#endif
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fprintf(stderr, "Region Layer\n");
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@ -43,64 +43,69 @@ void forward_region_layer(const region_layer l, network_state state)
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{
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int locations = l.side*l.side;
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int i,j;
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memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
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for(i = 0; i < l.batch*locations; ++i){
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for(j = 0; j < l.n; ++j){
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int in_index = i*l.n*l.coords + j*l.coords;
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int out_index = i*l.n*5 + j*5;
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float prob = state.input[in_index+0];
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float x = state.input[in_index+1];
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float y = state.input[in_index+2];
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float w = state.input[in_index+3];
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float h = state.input[in_index+4];
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/*
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float min_w = state.input[in_index+5];
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float max_w = state.input[in_index+6];
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float min_h = state.input[in_index+7];
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float max_h = state.input[in_index+8];
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*/
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l.output[out_index+0] = prob;
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l.output[out_index+1] = x;
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l.output[out_index+2] = y;
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l.output[out_index+3] = w;
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l.output[out_index+4] = h;
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int index = i*((1+l.coords)*l.n + l.classes);
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if(l.softmax){
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activate_array(l.output + index, l.n*(1+l.coords), LOGISTIC);
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int offset = l.n*(1+l.coords);
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softmax_array(l.output + index + offset, l.classes,
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l.output + index + offset);
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}
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}
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if(state.train){
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float avg_iou = 0;
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float avg_cat = 0;
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float avg_obj = 0;
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float avg_anyobj = 0;
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int count = 0;
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*(l.cost) = 0;
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int size = l.inputs * l.batch;
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memset(l.delta, 0, size * sizeof(float));
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for (i = 0; i < l.batch*locations; ++i) {
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int index = i*((1+l.coords)*l.n + l.classes);
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for(j = 0; j < l.n; ++j){
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int in_index = i*l.n*l.coords + j*l.coords;
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l.delta[in_index+0] = .1*(0-state.input[in_index+0]);
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int prob_index = index + j*(1 + l.coords);
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l.delta[prob_index] = (1./l.n)*(0-l.output[prob_index]);
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if(l.softmax){
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l.delta[prob_index] = 1./(l.n*l.side)*(0-l.output[prob_index]);
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}
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*(l.cost) += (1./l.n)*pow(l.output[prob_index], 2);
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//printf("%f\n", l.output[prob_index]);
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avg_anyobj += l.output[prob_index];
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}
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int truth_index = i*5;
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int truth_index = i*(1 + l.coords + l.classes);
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int best_index = -1;
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float best_iou = 0;
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float best_rmse = 4;
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int bg = !state.truth[truth_index];
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if(bg) continue;
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if(bg) {
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continue;
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}
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box truth = {state.truth[truth_index+1], state.truth[truth_index+2], state.truth[truth_index+3], state.truth[truth_index+4]};
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int class_index = index + l.n*(1+l.coords);
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for(j = 0; j < l.classes; ++j) {
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l.delta[class_index+j] = state.truth[truth_index+1+j] - l.output[class_index+j];
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*(l.cost) += pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2);
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if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j];
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}
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truth_index += l.classes + 1;
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box truth = {state.truth[truth_index+0], state.truth[truth_index+1], state.truth[truth_index+2], state.truth[truth_index+3]};
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truth.x /= l.side;
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truth.y /= l.side;
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for(j = 0; j < l.n; ++j){
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int out_index = i*l.n*5 + j*5;
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int out_index = index + j*(1+l.coords);
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box out = {l.output[out_index+1], l.output[out_index+2], l.output[out_index+3], l.output[out_index+4]};
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//printf("\n%f %f %f %f %f\n", l.output[out_index+0], out.x, out.y, out.w, out.h);
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out.x /= l.side;
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out.y /= l.side;
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if (l.sqrt){
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out.w = out.w*out.w;
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out.h = out.h*out.h;
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}
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float iou = box_iou(out, truth);
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float rmse = box_rmse(out, truth);
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@ -116,46 +121,41 @@ void forward_region_layer(const region_layer l, network_state state)
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}
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}
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}
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printf("%d", best_index);
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//int out_index = i*l.n*5 + best_index*5;
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//box out = {l.output[out_index+1], l.output[out_index+2], l.output[out_index+3], l.output[out_index+4]};
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int in_index = i*l.n*l.coords + best_index*l.coords;
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l.delta[in_index+0] = (1-state.input[in_index+0]);
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l.delta[in_index+1] = state.truth[truth_index+1] - state.input[in_index+1];
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l.delta[in_index+2] = state.truth[truth_index+2] - state.input[in_index+2];
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l.delta[in_index+3] = state.truth[truth_index+3] - state.input[in_index+3];
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l.delta[in_index+4] = state.truth[truth_index+4] - state.input[in_index+4];
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/*
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l.delta[in_index+5] = 0 - state.input[in_index+5];
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l.delta[in_index+6] = 1 - state.input[in_index+6];
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l.delta[in_index+7] = 0 - state.input[in_index+7];
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l.delta[in_index+8] = 1 - state.input[in_index+8];
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*/
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/*
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float x = state.input[in_index+1];
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float y = state.input[in_index+2];
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float w = state.input[in_index+3];
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float h = state.input[in_index+4];
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float min_w = state.input[in_index+5];
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float max_w = state.input[in_index+6];
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float min_h = state.input[in_index+7];
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float max_h = state.input[in_index+8];
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*/
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//printf("%d", best_index);
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int in_index = index + best_index*(1+l.coords);
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*(l.cost) -= pow(l.output[in_index], 2);
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*(l.cost) += pow(1-l.output[in_index], 2);
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avg_obj += l.output[in_index];
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l.delta[in_index+0] = (1.-l.output[in_index]);
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if(l.softmax){
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l.delta[in_index+0] = 5*(1.-l.output[in_index]);
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}
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//printf("%f\n", l.output[in_index]);
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l.delta[in_index+1] = 5*(state.truth[truth_index+0] - l.output[in_index+1]);
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l.delta[in_index+2] = 5*(state.truth[truth_index+1] - l.output[in_index+2]);
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if(l.sqrt){
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l.delta[in_index+3] = 5*(sqrt(state.truth[truth_index+2]) - l.output[in_index+3]);
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l.delta[in_index+4] = 5*(sqrt(state.truth[truth_index+3]) - l.output[in_index+4]);
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}else{
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l.delta[in_index+3] = 5*(state.truth[truth_index+2] - l.output[in_index+3]);
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l.delta[in_index+4] = 5*(state.truth[truth_index+3] - l.output[in_index+4]);
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}
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*(l.cost) += pow(1-best_iou, 2);
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avg_iou += best_iou;
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++count;
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if(l.softmax){
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gradient_array(l.output + index, l.n*(1+l.coords), LOGISTIC, l.delta + index);
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}
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printf("\nAvg IOU: %f %d\n", avg_iou/count, count);
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}
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printf("Avg IOU: %f, Avg Cat Pred: %f, Avg Obj: %f, Avg Any: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count);
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}
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}
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void backward_region_layer(const region_layer l, network_state state)
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{
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axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
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//copy_cpu(l.batch*l.inputs, l.delta, 1, state.delta, 1);
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}
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#ifdef GPU
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@ -165,8 +165,9 @@ void forward_region_layer_gpu(const region_layer l, network_state state)
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float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
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float *truth_cpu = 0;
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if(state.truth){
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truth_cpu = calloc(l.batch*l.outputs, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, l.batch*l.outputs);
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int num_truth = l.batch*l.side*l.side*(1+l.coords+l.classes);
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truth_cpu = calloc(num_truth, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, num_truth);
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}
|
||||
cuda_pull_array(state.input, in_cpu, l.batch*l.inputs);
|
||||
network_state cpu_state;
|
||||
|
||||
@ -11,7 +11,7 @@
|
||||
|
||||
char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
|
||||
|
||||
void draw_yoloplus(image im, float *box, int side, int objectness, char *label, float thresh)
|
||||
void draw_swag(image im, float *box, int side, int objectness, char *label, float thresh)
|
||||
{
|
||||
int classes = 20;
|
||||
int elems = 4+classes+objectness;
|
||||
@ -52,7 +52,7 @@ void draw_yoloplus(image im, float *box, int side, int objectness, char *label,
|
||||
show_image(im, label);
|
||||
}
|
||||
|
||||
void train_yoloplus(char *cfgfile, char *weightfile)
|
||||
void train_swag(char *cfgfile, char *weightfile)
|
||||
{
|
||||
char *train_images = "/home/pjreddie/data/voc/test/train.txt";
|
||||
char *backup_directory = "/home/pjreddie/backup/";
|
||||
@ -65,23 +65,20 @@ void train_yoloplus(char *cfgfile, char *weightfile)
|
||||
if(weightfile){
|
||||
load_weights(&net, weightfile);
|
||||
}
|
||||
detection_layer layer = get_network_detection_layer(net);
|
||||
int imgs = 128;
|
||||
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
|
||||
int imgs = net.batch*net.subdivisions;
|
||||
int i = *net.seen/imgs;
|
||||
|
||||
char **paths;
|
||||
list *plist = get_paths(train_images);
|
||||
int N = plist->size;
|
||||
paths = (char **)list_to_array(plist);
|
||||
|
||||
if(i*imgs > N*120){
|
||||
net.layers[net.n-1].rescore = 1;
|
||||
}
|
||||
data train, buffer;
|
||||
|
||||
int classes = layer.classes;
|
||||
int background = layer.objectness;
|
||||
int side = sqrt(get_detection_layer_locations(layer));
|
||||
|
||||
layer l = net.layers[net.n - 1];
|
||||
|
||||
int side = l.side;
|
||||
int classes = l.classes;
|
||||
|
||||
list *plist = get_paths(train_images);
|
||||
int N = plist->size;
|
||||
char **paths = (char **)list_to_array(plist);
|
||||
|
||||
load_args args = {0};
|
||||
args.w = net.w;
|
||||
@ -91,12 +88,12 @@ void train_yoloplus(char *cfgfile, char *weightfile)
|
||||
args.m = plist->size;
|
||||
args.classes = classes;
|
||||
args.num_boxes = side;
|
||||
args.background = background;
|
||||
args.d = &buffer;
|
||||
args.type = DETECTION_DATA;
|
||||
args.type = REGION_DATA;
|
||||
|
||||
pthread_t load_thread = load_data_in_thread(args);
|
||||
clock_t time;
|
||||
//while(i*imgs < N*120){
|
||||
while(get_current_batch(net) < net.max_batches){
|
||||
i += 1;
|
||||
time=clock();
|
||||
@ -105,36 +102,21 @@ void train_yoloplus(char *cfgfile, char *weightfile)
|
||||
load_thread = load_data_in_thread(args);
|
||||
|
||||
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
||||
|
||||
/*
|
||||
image im = float_to_image(net.w, net.h, 3, train.X.vals[113]);
|
||||
image copy = copy_image(im);
|
||||
draw_swag(copy, train.y.vals[113], 7, "truth");
|
||||
cvWaitKey(0);
|
||||
free_image(copy);
|
||||
*/
|
||||
|
||||
time=clock();
|
||||
float loss = train_network(net, train);
|
||||
if (avg_loss < 0) avg_loss = loss;
|
||||
avg_loss = avg_loss*.9 + loss*.1;
|
||||
|
||||
printf("%d: %f, %f avg, %lf seconds, %f rate, %d images, epoch: %f\n", get_current_batch(net), loss, avg_loss, sec(clock()-time), get_current_rate(net), *net.seen, (float)*net.seen/N);
|
||||
|
||||
if((i-1)*imgs <= 80*N && i*imgs > N*80){
|
||||
fprintf(stderr, "Second stage done.\n");
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s_second_stage.weights", backup_directory, base);
|
||||
save_weights(net, buff);
|
||||
net.layers[net.n-1].joint = 1;
|
||||
net.layers[net.n-1].objectness = 0;
|
||||
background = 0;
|
||||
|
||||
pthread_join(load_thread, 0);
|
||||
free_data(buffer);
|
||||
args.background = background;
|
||||
load_thread = load_data_in_thread(args);
|
||||
}
|
||||
|
||||
if((i-1)*imgs <= 120*N && i*imgs > N*120){
|
||||
fprintf(stderr, "Third stage done.\n");
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
|
||||
net.layers[net.n-1].rescore = 1;
|
||||
save_weights(net, buff);
|
||||
}
|
||||
|
||||
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
|
||||
if(i%1000==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
|
||||
@ -143,36 +125,38 @@ void train_yoloplus(char *cfgfile, char *weightfile)
|
||||
free_data(train);
|
||||
}
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s_rescore.weights", backup_directory, base);
|
||||
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
|
||||
save_weights(net, buff);
|
||||
}
|
||||
|
||||
void convert_yoloplus_detections(float *predictions, int classes, int objectness, int background, int num_boxes, int w, int h, float thresh, float **probs, box *boxes)
|
||||
void convert_swag_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes)
|
||||
{
|
||||
int i,j;
|
||||
int per_box = 4+classes+(background || objectness);
|
||||
for (i = 0; i < num_boxes*num_boxes; ++i){
|
||||
float scale = 1;
|
||||
if(objectness) scale = 1-predictions[i*per_box];
|
||||
int offset = i*per_box+(background||objectness);
|
||||
int i,j,n;
|
||||
int per_cell = 5*num+classes;
|
||||
for (i = 0; i < side*side; ++i){
|
||||
int row = i / side;
|
||||
int col = i % side;
|
||||
for(n = 0; n < num; ++n){
|
||||
int offset = i*per_cell + 5*n;
|
||||
float scale = predictions[offset];
|
||||
int index = i*num + n;
|
||||
boxes[index].x = (predictions[offset + 1] + col) / side * w;
|
||||
boxes[index].y = (predictions[offset + 2] + row) / side * h;
|
||||
boxes[index].w = pow(predictions[offset + 3], (square?2:1)) * w;
|
||||
boxes[index].h = pow(predictions[offset + 4], (square?2:1)) * h;
|
||||
for(j = 0; j < classes; ++j){
|
||||
offset = i*per_cell + 5*num;
|
||||
float prob = scale*predictions[offset+j];
|
||||
probs[i][j] = (prob > thresh) ? prob : 0;
|
||||
probs[index][j] = (prob > thresh) ? prob : 0;
|
||||
}
|
||||
}
|
||||
int row = i / num_boxes;
|
||||
int col = i % num_boxes;
|
||||
offset += classes;
|
||||
boxes[i].x = (predictions[offset + 0] + col) / num_boxes * w;
|
||||
boxes[i].y = (predictions[offset + 1] + row) / num_boxes * h;
|
||||
boxes[i].w = pow(predictions[offset + 2], 2) * w;
|
||||
boxes[i].h = pow(predictions[offset + 3], 2) * h;
|
||||
}
|
||||
}
|
||||
|
||||
void print_yoloplus_detections(FILE **fps, char *id, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
|
||||
void print_swag_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h)
|
||||
{
|
||||
int i, j;
|
||||
for(i = 0; i < num_boxes*num_boxes; ++i){
|
||||
for(i = 0; i < total; ++i){
|
||||
float xmin = boxes[i].x - boxes[i].w/2.;
|
||||
float xmax = boxes[i].x + boxes[i].w/2.;
|
||||
float ymin = boxes[i].y - boxes[i].h/2.;
|
||||
@ -190,14 +174,13 @@ void print_yoloplus_detections(FILE **fps, char *id, box *boxes, float **probs,
|
||||
}
|
||||
}
|
||||
|
||||
void validate_yoloplus(char *cfgfile, char *weightfile)
|
||||
void validate_swag(char *cfgfile, char *weightfile)
|
||||
{
|
||||
network net = parse_network_cfg(cfgfile);
|
||||
if(weightfile){
|
||||
load_weights(&net, weightfile);
|
||||
}
|
||||
set_batch_network(&net, 1);
|
||||
detection_layer layer = get_network_detection_layer(net);
|
||||
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
|
||||
srand(time(0));
|
||||
|
||||
@ -205,10 +188,10 @@ void validate_yoloplus(char *cfgfile, char *weightfile)
|
||||
list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
|
||||
char **paths = (char **)list_to_array(plist);
|
||||
|
||||
int classes = layer.classes;
|
||||
int objectness = layer.objectness;
|
||||
int background = layer.background;
|
||||
int num_boxes = sqrt(get_detection_layer_locations(layer));
|
||||
layer l = net.layers[net.n-1];
|
||||
int classes = l.classes;
|
||||
int square = l.sqrt;
|
||||
int side = l.side;
|
||||
|
||||
int j;
|
||||
FILE **fps = calloc(classes, sizeof(FILE *));
|
||||
@ -217,9 +200,9 @@ void validate_yoloplus(char *cfgfile, char *weightfile)
|
||||
snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
|
||||
fps[j] = fopen(buff, "w");
|
||||
}
|
||||
box *boxes = calloc(num_boxes*num_boxes, sizeof(box));
|
||||
float **probs = calloc(num_boxes*num_boxes, sizeof(float *));
|
||||
for(j = 0; j < num_boxes*num_boxes; ++j) probs[j] = calloc(classes, sizeof(float *));
|
||||
box *boxes = calloc(side*side*l.n, sizeof(box));
|
||||
float **probs = calloc(side*side*l.n, sizeof(float *));
|
||||
for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
|
||||
|
||||
int m = plist->size;
|
||||
int i=0;
|
||||
@ -268,9 +251,9 @@ void validate_yoloplus(char *cfgfile, char *weightfile)
|
||||
float *predictions = network_predict(net, X);
|
||||
int w = val[t].w;
|
||||
int h = val[t].h;
|
||||
convert_yoloplus_detections(predictions, classes, objectness, background, num_boxes, w, h, thresh, probs, boxes);
|
||||
if (nms) do_nms(boxes, probs, num_boxes*num_boxes, classes, iou_thresh);
|
||||
print_yoloplus_detections(fps, id, boxes, probs, num_boxes, classes, w, h);
|
||||
convert_swag_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes);
|
||||
if (nms) do_nms(boxes, probs, side*side*l.n, classes, iou_thresh);
|
||||
print_swag_detections(fps, id, boxes, probs, side*side*l.n, classes, w, h);
|
||||
free(id);
|
||||
free_image(val[t]);
|
||||
free_image(val_resized[t]);
|
||||
@ -279,7 +262,7 @@ void validate_yoloplus(char *cfgfile, char *weightfile)
|
||||
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
|
||||
}
|
||||
|
||||
void test_yoloplus(char *cfgfile, char *weightfile, char *filename, float thresh)
|
||||
void test_swag(char *cfgfile, char *weightfile, char *filename, float thresh)
|
||||
{
|
||||
|
||||
network net = parse_network_cfg(cfgfile);
|
||||
@ -306,7 +289,7 @@ void test_yoloplus(char *cfgfile, char *weightfile, char *filename, float thresh
|
||||
time=clock();
|
||||
float *predictions = network_predict(net, X);
|
||||
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
|
||||
draw_yoloplus(im, predictions, 7, layer.objectness, "predictions", thresh);
|
||||
draw_swag(im, predictions, 7, layer.objectness, "predictions", thresh);
|
||||
free_image(im);
|
||||
free_image(sized);
|
||||
#ifdef OPENCV
|
||||
@ -317,7 +300,7 @@ void test_yoloplus(char *cfgfile, char *weightfile, char *filename, float thresh
|
||||
}
|
||||
}
|
||||
|
||||
void run_yoloplus(int argc, char **argv)
|
||||
void run_swag(int argc, char **argv)
|
||||
{
|
||||
float thresh = find_float_arg(argc, argv, "-thresh", .2);
|
||||
if(argc < 4){
|
||||
@ -328,7 +311,7 @@ void run_yoloplus(int argc, char **argv)
|
||||
char *cfg = argv[3];
|
||||
char *weights = (argc > 4) ? argv[4] : 0;
|
||||
char *filename = (argc > 5) ? argv[5]: 0;
|
||||
if(0==strcmp(argv[2], "test")) test_yoloplus(cfg, weights, filename, thresh);
|
||||
else if(0==strcmp(argv[2], "train")) train_yoloplus(cfg, weights);
|
||||
else if(0==strcmp(argv[2], "valid")) validate_yoloplus(cfg, weights);
|
||||
if(0==strcmp(argv[2], "test")) test_swag(cfg, weights, filename, thresh);
|
||||
else if(0==strcmp(argv[2], "train")) train_swag(cfg, weights);
|
||||
else if(0==strcmp(argv[2], "valid")) validate_swag(cfg, weights);
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user