diff --git a/Makefile b/Makefile
index b36b6b85c83ef759a5e4f3a72994da53585cf945..0a48e5508df4559ada2ab7025c38b27de8c74bd5 100644
--- a/Makefile
+++ b/Makefile
@@ -41,10 +41,10 @@ CFLAGS+= -DCUDNN
LDFLAGS+= -lcudnn
endif
-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 captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o super.o voxel.o
+OBJ=gemm.o utils.o cuda.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 captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o super.o voxel.o
ifeq ($(GPU), 1)
LDFLAGS+= -lstdc++
-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
+OBJ+=convolutional_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
endif
OBJS = $(addprefix $(OBJDIR), $(OBJ))
diff --git a/data/labels/make_labels.py b/data/labels/make_labels.py
index bdd2421f6b2a9eb2a9690bcd61bd55a45b49a674..1dacdc376bfca579a540f19c4f8dac80bb7c39bd 100644
--- a/data/labels/make_labels.py
+++ b/data/labels/make_labels.py
@@ -1,6 +1,19 @@
import os
+import string
+import pipes
-l = ["person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
+#l = ["person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
+
+l = string.printable
for word in l:
- os.system("convert -fill black -background white -bordercolor white -border 4 -font futura-normal -pointsize 18 label:\"%s\" \"%s.png\""%(word, word))
+ #os.system("convert -fill black -background white -bordercolor white -border 4 -font futura-normal -pointsize 18 label:\"%s\" \"%s.png\""%(word, word))
+ if word == ' ':
+ os.system('convert -fill black -background white -bordercolor white -font futura-normal -pointsize 64 label:"\ " 32.png')
+ elif word == '\\':
+ os.system('convert -fill black -background white -bordercolor white -font futura-normal -pointsize 64 label:"\\\\\\\\" 92.png')
+ elif ord(word) in [9,10,11,12,13,14]:
+ pass
+ else:
+ os.system("convert -fill black -background white -bordercolor white -font futura-normal -pointsize 64 label:%s \"%d.png\""%(pipes.quote(word), ord(word)))
+
diff --git a/src/activation_layer.c b/src/activation_layer.c
index 49e638d45764c8759123052c695348ec988c5f83..3430dac407846c995f60789c5579c7eb6517efd2 100644
--- a/src/activation_layer.c
+++ b/src/activation_layer.c
@@ -21,7 +21,12 @@ layer make_activation_layer(int batch, int inputs, ACTIVATION activation)
l.output = calloc(batch*inputs, sizeof(float*));
l.delta = calloc(batch*inputs, sizeof(float*));
+ l.forward = forward_activation_layer;
+ l.backward = backward_activation_layer;
#ifdef GPU
+ l.forward_gpu = forward_activation_layer_gpu;
+ l.backward_gpu = backward_activation_layer_gpu;
+
l.output_gpu = cuda_make_array(l.output, inputs*batch);
l.delta_gpu = cuda_make_array(l.delta, inputs*batch);
#endif
diff --git a/src/art.c b/src/art.c
index 9a0559e5ab0ec9cdaf6bccb3fc3931d10346fc1d..71d37192557b649297c04209f476e00347f0cb1c 100644
--- a/src/art.c
+++ b/src/art.c
@@ -8,6 +8,7 @@
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
+image get_image_from_stream(CvCapture *cap);
#endif
diff --git a/src/avgpool_layer.c b/src/avgpool_layer.c
index 0feae710f9243f686069e41e1315a7fc4fbb82a1..c6db477e74e9cc14bbb2bd91a799701c24e20e8b 100644
--- a/src/avgpool_layer.c
+++ b/src/avgpool_layer.c
@@ -19,7 +19,11 @@ avgpool_layer make_avgpool_layer(int batch, int w, int h, int c)
int output_size = l.outputs * batch;
l.output = calloc(output_size, sizeof(float));
l.delta = calloc(output_size, sizeof(float));
+ l.forward = forward_avgpool_layer;
+ l.backward = backward_avgpool_layer;
#ifdef GPU
+ l.forward_gpu = forward_avgpool_layer_gpu;
+ l.backward_gpu = backward_avgpool_layer_gpu;
l.output_gpu = cuda_make_array(l.output, output_size);
l.delta_gpu = cuda_make_array(l.delta, output_size);
#endif
diff --git a/src/batchnorm_layer.c b/src/batchnorm_layer.c
index 9b68277efbdb999575e6f4725b4d7148c85e07e5..510f1b2feb052083a003c16a58028d2ce0dbec06 100644
--- a/src/batchnorm_layer.c
+++ b/src/batchnorm_layer.c
@@ -28,7 +28,13 @@ layer make_batchnorm_layer(int batch, int w, int h, int c)
layer.rolling_mean = calloc(c, sizeof(float));
layer.rolling_variance = calloc(c, sizeof(float));
+
+ layer.forward = forward_batchnorm_layer;
+ layer.backward = backward_batchnorm_layer;
#ifdef GPU
+ layer.forward_gpu = forward_batchnorm_layer_gpu;
+ layer.backward_gpu = backward_batchnorm_layer_gpu;
+
layer.output_gpu = cuda_make_array(layer.output, h * w * c * batch);
layer.delta_gpu = cuda_make_array(layer.delta, h * w * c * batch);
diff --git a/src/classifier.c b/src/classifier.c
index b42d010844c10c1c7ee83d7c73b20fe9bc163601..208b7ed4ddbb3e9494d712a5b3b618d71ccbe100 100644
--- a/src/classifier.c
+++ b/src/classifier.c
@@ -10,6 +10,7 @@
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
+image get_image_from_stream(CvCapture *cap);
#endif
list *read_data_cfg(char *filename)
@@ -57,25 +58,26 @@ void train_classifier_multi(char *datacfg, char *cfgfile, char *weightfile, int
#ifdef GPU
int i;
- srand(time(0));
float avg_loss = -1;
char *base = basecfg(cfgfile);
printf("%s\n", base);
printf("%d\n", ngpus);
network *nets = calloc(ngpus, sizeof(network));
+
+ srand(time(0));
+ int seed = rand();
for(i = 0; i < ngpus; ++i){
+ srand(seed);
cuda_set_device(gpus[i]);
nets[i] = parse_network_cfg(cfgfile);
- if(clear) *nets[i].seen = 0;
if(weightfile){
load_weights(&nets[i], weightfile);
}
- }
- network net = nets[0];
- for(i = 0; i < ngpus; ++i){
- *nets[i].seen = *net.seen;
+ if(clear) *nets[i].seen = 0;
nets[i].learning_rate *= ngpus;
}
+ srand(time(0));
+ network net = nets[0];
int imgs = net.batch * net.subdivisions * ngpus;
diff --git a/src/coco.c b/src/coco.c
index b78d640239c2b008afdb6db9daa812589bae1ae2..939a08d52e3a1f1b9e53d3d42f742c4fe1bb47e2 100644
--- a/src/coco.c
+++ b/src/coco.c
@@ -12,14 +12,10 @@
#include "opencv2/highgui/highgui_c.h"
#endif
-void convert_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness);
-
char *coco_classes[] = {"person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush"};
int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
-image coco_labels[80];
-
void train_coco(char *cfgfile, char *weightfile)
{
//char *train_images = "/home/pjreddie/data/voc/test/train.txt";
@@ -160,7 +156,6 @@ void validate_coco(char *cfgfile, char *weightfile)
layer l = net.layers[net.n-1];
int classes = l.classes;
- int square = l.sqrt;
int side = l.side;
int j;
@@ -217,10 +212,10 @@ void validate_coco(char *cfgfile, char *weightfile)
char *path = paths[i+t-nthreads];
int image_id = get_coco_image_id(path);
float *X = val_resized[t].data;
- float *predictions = network_predict(net, X);
+ network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
- convert_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes, 0);
+ get_detection_boxes(l, w, h, thresh, probs, boxes, 0);
if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, iou_thresh);
print_cocos(fp, image_id, boxes, probs, side*side*l.n, classes, w, h);
free_image(val[t]);
@@ -250,7 +245,6 @@ void validate_coco_recall(char *cfgfile, char *weightfile)
layer l = net.layers[net.n-1];
int classes = l.classes;
- int square = l.sqrt;
int side = l.side;
int j, k;
@@ -282,14 +276,15 @@ void validate_coco_recall(char *cfgfile, char *weightfile)
image orig = load_image_color(path, 0, 0);
image sized = resize_image(orig, net.w, net.h);
char *id = basecfg(path);
- float *predictions = network_predict(net, sized.data);
- convert_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 1);
+ network_predict(net, sized.data);
+ get_detection_boxes(l, 1, 1, thresh, probs, boxes, 1);
if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh);
- char *labelpath = find_replace(path, "images", "labels");
- labelpath = find_replace(labelpath, "JPEGImages", "labels");
- labelpath = find_replace(labelpath, ".jpg", ".txt");
- labelpath = find_replace(labelpath, ".JPEG", ".txt");
+ char labelpath[4096];
+ find_replace(path, "images", "labels", labelpath);
+ find_replace(labelpath, "JPEGImages", "labels", labelpath);
+ find_replace(labelpath, ".jpg", ".txt", labelpath);
+ find_replace(labelpath, ".JPEG", ".txt", labelpath);
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
@@ -323,7 +318,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile)
void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
{
-
+ image *alphabet = load_alphabet();
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
@@ -353,11 +348,11 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
image sized = resize_image(im, net.w, net.h);
float *X = sized.data;
time=clock();
- float *predictions = network_predict(net, X);
+ network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
- convert_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0);
+ get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0);
if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
- draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, coco_classes, coco_labels, 80);
+ draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, coco_classes, alphabet, 80);
save_image(im, "prediction");
show_image(im, "predictions");
free_image(im);
@@ -372,12 +367,7 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
void run_coco(int argc, char **argv)
{
- int i;
- for(i = 0; i < 80; ++i){
- char buff[256];
- sprintf(buff, "data/labels/%s.png", coco_classes[i]);
- coco_labels[i] = load_image_color(buff, 0, 0);
- }
+ char *prefix = find_char_arg(argc, argv, "-prefix", 0);
float thresh = find_float_arg(argc, argv, "-thresh", .2);
int cam_index = find_int_arg(argc, argv, "-c", 0);
int frame_skip = find_int_arg(argc, argv, "-s", 0);
@@ -394,5 +384,5 @@ void run_coco(int argc, char **argv)
else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights);
else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights);
- else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, coco_classes, coco_labels, 80, frame_skip);
+ else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, coco_classes, 80, frame_skip, prefix);
}
diff --git a/src/connected_layer.c b/src/connected_layer.c
index f46c3e1888ff9864d91c288d548d601d97a8be78..2694229214b7389bc27ae2fe4bad303841a72181 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -36,6 +36,10 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT
l.weights = calloc(outputs*inputs, sizeof(float));
l.biases = calloc(outputs, sizeof(float));
+ l.forward = forward_connected_layer;
+ l.backward = backward_connected_layer;
+ l.update = update_connected_layer;
+
//float scale = 1./sqrt(inputs);
float scale = sqrt(2./inputs);
for(i = 0; i < outputs*inputs; ++i){
@@ -66,6 +70,10 @@ connected_layer make_connected_layer(int batch, int inputs, int outputs, ACTIVAT
}
#ifdef GPU
+ l.forward_gpu = forward_connected_layer_gpu;
+ l.backward_gpu = backward_connected_layer_gpu;
+ l.update_gpu = update_connected_layer_gpu;
+
l.weights_gpu = cuda_make_array(l.weights, outputs*inputs);
l.biases_gpu = cuda_make_array(l.biases, outputs);
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 01bb700c4db595e97dcb2b2d73a2357710689222..ef9c093c639cd0a8c230e1022f59a13d6ece3eb2 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -209,6 +209,9 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float));
+ l.forward = forward_convolutional_layer;
+ l.backward = backward_convolutional_layer;
+ l.update = update_convolutional_layer;
if(binary){
l.binary_weights = calloc(c*n*size*size, sizeof(float));
l.cweights = calloc(c*n*size*size, sizeof(char));
@@ -234,6 +237,10 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
}
#ifdef GPU
+ l.forward_gpu = forward_convolutional_layer_gpu;
+ l.backward_gpu = backward_convolutional_layer_gpu;
+ l.update_gpu = update_convolutional_layer_gpu;
+
if(gpu_index >= 0){
l.weights_gpu = cuda_make_array(l.weights, c*n*size*size);
l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size);
diff --git a/src/cost_layer.c b/src/cost_layer.c
index 0d8cb8c15fcad42cdc00ce24f95675676f785f22..f266c6a16a11044bd77f718d612981be9f99e896 100644
--- a/src/cost_layer.c
+++ b/src/cost_layer.c
@@ -43,7 +43,13 @@ cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float sca
l.delta = calloc(inputs*batch, sizeof(float));
l.output = calloc(inputs*batch, sizeof(float));
l.cost = calloc(1, sizeof(float));
+
+ l.forward = forward_cost_layer;
+ l.backward = backward_cost_layer;
#ifdef GPU
+ l.forward_gpu = forward_cost_layer_gpu;
+ l.backward_gpu = backward_cost_layer_gpu;
+
l.delta_gpu = cuda_make_array(l.output, inputs*batch);
l.output_gpu = cuda_make_array(l.delta, inputs*batch);
#endif
diff --git a/src/crnn_layer.c b/src/crnn_layer.c
index 5d5fa636a5296becc1ffa32b48b7ccd194b2304d..febff63f4b6dfdc7a3fd008dc75fe2016aa9f424 100644
--- a/src/crnn_layer.c
+++ b/src/crnn_layer.c
@@ -64,7 +64,15 @@ layer make_crnn_layer(int batch, int h, int w, int c, int hidden_filters, int ou
l.output = l.output_layer->output;
l.delta = l.output_layer->delta;
+ l.forward = forward_crnn_layer;
+ l.backward = backward_crnn_layer;
+ l.update = update_crnn_layer;
+
#ifdef GPU
+ l.forward_gpu = forward_crnn_layer_gpu;
+ l.backward_gpu = backward_crnn_layer_gpu;
+ l.update_gpu = update_crnn_layer_gpu;
+
l.state_gpu = cuda_make_array(l.state, l.hidden*batch*(steps+1));
l.output_gpu = l.output_layer->output_gpu;
l.delta_gpu = l.output_layer->delta_gpu;
diff --git a/src/crop_layer.c b/src/crop_layer.c
index 66f11ebc9f25a1dce71a45767a4a536534cb3b00..11c59b491f26f413c926b4354a21d2aebfa9243d 100644
--- a/src/crop_layer.c
+++ b/src/crop_layer.c
@@ -10,6 +10,9 @@ image get_crop_image(crop_layer l)
return float_to_image(w,h,c,l.output);
}
+void backward_crop_layer(const crop_layer l, network_state state){}
+void backward_crop_layer_gpu(const crop_layer l, network_state state){}
+
crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip, float angle, float saturation, float exposure)
{
fprintf(stderr, "Crop Layer: %d x %d -> %d x %d x %d image\n", h,w,crop_height,crop_width,c);
@@ -30,7 +33,12 @@ crop_layer make_crop_layer(int batch, int h, int w, int c, int crop_height, int
l.inputs = l.w * l.h * l.c;
l.outputs = l.out_w * l.out_h * l.out_c;
l.output = calloc(l.outputs*batch, sizeof(float));
+ l.forward = forward_crop_layer;
+ l.backward = backward_crop_layer;
+
#ifdef GPU
+ l.forward_gpu = forward_crop_layer_gpu;
+ l.backward_gpu = backward_crop_layer_gpu;
l.output_gpu = cuda_make_array(l.output, l.outputs*batch);
l.rand_gpu = cuda_make_array(0, l.batch*8);
#endif
diff --git a/src/darknet.c b/src/darknet.c
index 1b7232982fd1b26c071447a4c08d1ca39e5e6b77..3bc0c6a7a90fc33559b1a9411e618373e09a0dde 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -136,17 +136,6 @@ void partial(char *cfgfile, char *weightfile, char *outfile, int max)
save_weights_upto(net, outfile, max);
}
-void stacked(char *cfgfile, char *weightfile, char *outfile)
-{
- gpu_index = -1;
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- net.seen = 0;
- save_weights_double(net, outfile);
-}
-
#include "convolutional_layer.h"
void rescale_net(char *cfgfile, char *weightfile, char *outfile)
{
@@ -420,8 +409,6 @@ int main(int argc, char **argv)
partial(argv[2], argv[3], argv[4], atoi(argv[5]));
} else if (0 == strcmp(argv[1], "average")){
average(argc, argv);
- } else if (0 == strcmp(argv[1], "stacked")){
- stacked(argv[2], argv[3], argv[4]);
} else if (0 == strcmp(argv[1], "visualize")){
visualize(argv[2], (argc > 3) ? argv[3] : 0);
} else if (0 == strcmp(argv[1], "imtest")){
diff --git a/src/data.c b/src/data.c
index 5977a3fb3ceff5334cc9f786967b88c33b1e9365..20d57481dec74a46322d8ad7e7f4251f8280c7a8 100644
--- a/src/data.c
+++ b/src/data.c
@@ -47,7 +47,7 @@ char **get_random_paths(char **paths, int n, int m)
for(i = 0; i < n; ++i){
int index = rand()%m;
random_paths[i] = paths[index];
- if(i == 0) printf("%s\n", paths[index]);
+ //if(i == 0) printf("%s\n", paths[index]);
}
pthread_mutex_unlock(&mutex);
return random_paths;
@@ -58,7 +58,8 @@ char **find_replace_paths(char **paths, int n, char *find, char *replace)
char **replace_paths = calloc(n, sizeof(char*));
int i;
for(i = 0; i < n; ++i){
- char *replaced = find_replace(paths[i], find, replace);
+ char replaced[4096];
+ find_replace(paths[i], find, replace, replaced);
replace_paths[i] = copy_string(replaced);
}
return replace_paths;
@@ -198,12 +199,13 @@ void correct_boxes(box_label *boxes, int n, float dx, float dy, float sx, float
void fill_truth_swag(char *path, float *truth, int classes, int flip, float dx, float dy, float sx, float sy)
{
- char *labelpath = find_replace(path, "images", "labels");
- labelpath = find_replace(labelpath, "JPEGImages", "labels");
+ char labelpath[4096];
+ find_replace(path, "images", "labels", labelpath);
+ find_replace(labelpath, "JPEGImages", "labels", labelpath);
+ find_replace(labelpath, ".jpg", ".txt", labelpath);
+ find_replace(labelpath, ".JPG", ".txt", labelpath);
+ find_replace(labelpath, ".JPEG", ".txt", labelpath);
- labelpath = find_replace(labelpath, ".jpg", ".txt");
- labelpath = find_replace(labelpath, ".JPG", ".txt");
- labelpath = find_replace(labelpath, ".JPEG", ".txt");
int count = 0;
box_label *boxes = read_boxes(labelpath, &count);
randomize_boxes(boxes, count);
@@ -235,13 +237,14 @@ void fill_truth_swag(char *path, float *truth, int classes, int flip, float dx,
void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int flip, float dx, float dy, float sx, float sy)
{
- char *labelpath = find_replace(path, "images", "labels");
- labelpath = find_replace(labelpath, "JPEGImages", "labels");
-
- labelpath = find_replace(labelpath, ".jpg", ".txt");
- labelpath = find_replace(labelpath, ".png", ".txt");
- labelpath = find_replace(labelpath, ".JPG", ".txt");
- labelpath = find_replace(labelpath, ".JPEG", ".txt");
+ char labelpath[4096];
+ find_replace(path, "images", "labels", labelpath);
+ find_replace(labelpath, "JPEGImages", "labels", labelpath);
+
+ find_replace(labelpath, ".jpg", ".txt", labelpath);
+ find_replace(labelpath, ".png", ".txt", labelpath);
+ find_replace(labelpath, ".JPG", ".txt", labelpath);
+ find_replace(labelpath, ".JPEG", ".txt", labelpath);
int count = 0;
box_label *boxes = read_boxes(labelpath, &count);
randomize_boxes(boxes, count);
@@ -282,13 +285,14 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int
void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy)
{
- char *labelpath = find_replace(path, "images", "labels");
- labelpath = find_replace(labelpath, "JPEGImages", "labels");
-
- labelpath = find_replace(labelpath, ".jpg", ".txt");
- labelpath = find_replace(labelpath, ".png", ".txt");
- labelpath = find_replace(labelpath, ".JPG", ".txt");
- labelpath = find_replace(labelpath, ".JPEG", ".txt");
+ char labelpath[4096];
+ find_replace(path, "images", "labels", labelpath);
+ find_replace(labelpath, "JPEGImages", "labels", labelpath);
+
+ find_replace(labelpath, ".jpg", ".txt", labelpath);
+ find_replace(labelpath, ".png", ".txt", labelpath);
+ find_replace(labelpath, ".JPG", ".txt", labelpath);
+ find_replace(labelpath, ".JPEG", ".txt", labelpath);
int count = 0;
box_label *boxes = read_boxes(labelpath, &count);
randomize_boxes(boxes, count);
@@ -400,11 +404,12 @@ matrix load_tags_paths(char **paths, int n, int k)
int i;
int count = 0;
for(i = 0; i < n; ++i){
- char *label = find_replace(paths[i], "imgs", "labels");
- label = find_replace(label, "_iconl.jpeg", ".txt");
+ char label[4096];
+ find_replace(paths[i], "imgs", "labels", label);
+ find_replace(label, "_iconl.jpeg", ".txt", label);
FILE *file = fopen(label, "r");
if(!file){
- label = find_replace(label, "labels", "labels2");
+ find_replace(label, "labels", "labels2", label);
file = fopen(label, "r");
if(!file) continue;
}
@@ -518,16 +523,18 @@ data load_data_compare(int n, char **paths, int m, int classes, int w, int h)
int id;
float iou;
- char *imlabel1 = find_replace(paths[i*2], "imgs", "labels");
- imlabel1 = find_replace(imlabel1, "jpg", "txt");
+ char imlabel1[4096];
+ char imlabel2[4096];
+ find_replace(paths[i*2], "imgs", "labels", imlabel1);
+ find_replace(imlabel1, "jpg", "txt", imlabel1);
FILE *fp1 = fopen(imlabel1, "r");
while(fscanf(fp1, "%d %f", &id, &iou) == 2){
if (d.y.vals[i][2*id] < iou) d.y.vals[i][2*id] = iou;
}
- char *imlabel2 = find_replace(paths[i*2+1], "imgs", "labels");
- imlabel2 = find_replace(imlabel2, "jpg", "txt");
+ find_replace(paths[i*2+1], "imgs", "labels", imlabel2);
+ find_replace(imlabel2, "jpg", "txt", imlabel2);
FILE *fp2 = fopen(imlabel2, "r");
while(fscanf(fp2, "%d %f", &id, &iou) == 2){
@@ -709,6 +716,7 @@ void *load_threads(void *ptr)
{
int i;
load_args args = *(load_args *)ptr;
+ if (args.threads == 0) args.threads = 1;
data *out = args.d;
int total = args.n;
free(ptr);
diff --git a/src/deconvolutional_layer.c b/src/deconvolutional_layer.c
index 1262238fd00f03b2d213ef502ae78d0d61278244..fbef9d588291047bd74f735319955f3bac4a1d7a 100644
--- a/src/deconvolutional_layer.c
+++ b/src/deconvolutional_layer.c
@@ -80,6 +80,10 @@ deconvolutional_layer make_deconvolutional_layer(int batch, int h, int w, int c,
l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float));
+ l.forward = forward_deconvolutional_layer;
+ l.backward = backward_deconvolutional_layer;
+ l.update = update_deconvolutional_layer;
+
#ifdef GPU
l.weights_gpu = cuda_make_array(l.weights, c*n*size*size);
l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size);
diff --git a/src/demo.c b/src/demo.c
index 7c480b7dc6e00b7ce5399bda2a5673350a42dd3e..6c653a9f6005a4a3f707d1c361fa31705ac9fa7c 100644
--- a/src/demo.c
+++ b/src/demo.c
@@ -1,5 +1,6 @@
#include "network.h"
#include "detection_layer.h"
+#include "region_layer.h"
#include "cost_layer.h"
#include "utils.h"
#include "parser.h"
@@ -13,10 +14,10 @@
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/imgproc/imgproc_c.h"
-void convert_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness);
+image get_image_from_stream(CvCapture *cap);
static char **demo_names;
-static image *demo_labels;
+static image *demo_alphabet;
static int demo_classes;
static float **probs;
@@ -50,16 +51,23 @@ void *detect_in_thread(void *ptr)
{
float nms = .4;
- detection_layer l = net.layers[net.n-1];
+ layer l = net.layers[net.n-1];
float *X = det_s.data;
float *prediction = network_predict(net, X);
memcpy(predictions[demo_index], prediction, l.outputs*sizeof(float));
mean_arrays(predictions, FRAMES, l.outputs, avg);
+ l.output = avg;
free_image(det_s);
- convert_detections(avg, l.classes, l.n, l.sqrt, l.side, 1, 1, demo_thresh, probs, boxes, 0);
- if (nms > 0) do_nms(boxes, probs, l.side*l.side*l.n, l.classes, nms);
+ if(l.type == DETECTION){
+ get_detection_boxes(l, 1, 1, demo_thresh, probs, boxes, 0);
+ } else if (l.type == REGION){
+ get_region_boxes(l, 1, 1, demo_thresh, probs, boxes, 0);
+ } else {
+ error("Last layer must produce detections\n");
+ }
+ if (nms > 0) do_nms(boxes, probs, l.w*l.h*l.n, l.classes, nms);
printf("\033[2J");
printf("\033[1;1H");
printf("\nFPS:%.1f\n",fps);
@@ -69,7 +77,7 @@ void *detect_in_thread(void *ptr)
det = images[(demo_index + FRAMES/2 + 1)%FRAMES];
demo_index = (demo_index + 1)%FRAMES;
- draw_detections(det, l.side*l.side*l.n, demo_thresh, boxes, probs, demo_names, demo_labels, demo_classes);
+ draw_detections(det, l.w*l.h*l.n, demo_thresh, boxes, probs, demo_names, demo_alphabet, demo_classes);
return 0;
}
@@ -83,12 +91,13 @@ double get_wall_time()
return (double)time.tv_sec + (double)time.tv_usec * .000001;
}
-void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, image *labels, int classes, int frame_skip)
+void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix)
{
//skip = frame_skip;
+ image *alphabet = load_alphabet();
int delay = frame_skip;
demo_names = names;
- demo_labels = labels;
+ demo_alphabet = alphabet;
demo_classes = classes;
demo_thresh = thresh;
printf("Demo\n");
@@ -108,16 +117,16 @@ void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const ch
if(!cap) error("Couldn't connect to webcam.\n");
- detection_layer l = net.layers[net.n-1];
+ layer l = net.layers[net.n-1];
int j;
avg = (float *) calloc(l.outputs, sizeof(float));
for(j = 0; j < FRAMES; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float));
for(j = 0; j < FRAMES; ++j) images[j] = make_image(1,1,3);
- boxes = (box *)calloc(l.side*l.side*l.n, sizeof(box));
- probs = (float **)calloc(l.side*l.side*l.n, sizeof(float *));
- for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *));
+ boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box));
+ probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *));
+ for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *));
pthread_t fetch_thread;
pthread_t detect_thread;
@@ -141,9 +150,11 @@ void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const ch
}
int count = 0;
- cvNamedWindow("Demo", CV_WINDOW_NORMAL);
- cvMoveWindow("Demo", 0, 0);
- cvResizeWindow("Demo", 1352, 1013);
+ if(!prefix){
+ cvNamedWindow("Demo", CV_WINDOW_NORMAL);
+ cvMoveWindow("Demo", 0, 0);
+ cvResizeWindow("Demo", 1352, 1013);
+ }
double before = get_wall_time();
@@ -153,7 +164,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const ch
if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed");
if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed");
- if(1){
+ if(!prefix){
show_image(disp, "Demo");
int c = cvWaitKey(1);
if (c == 10){
@@ -164,7 +175,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const ch
}
}else{
char buff[256];
- sprintf(buff, "/home/pjreddie/tmp/bag_%07d", count);
+ sprintf(buff, "%s_%08d", prefix, count);
save_image(disp, buff);
}
@@ -201,7 +212,7 @@ void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const ch
}
}
#else
-void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, image *labels, int classes, int frame_skip)
+void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix)
{
fprintf(stderr, "Demo needs OpenCV for webcam images.\n");
}
diff --git a/src/demo.h b/src/demo.h
index 0e694bd582cf30b5969dd32c87cfa5ecc31656ad..5f922717157c44865ab3402d232de419b728b79d 100644
--- a/src/demo.h
+++ b/src/demo.h
@@ -2,6 +2,6 @@
#define DEMO
#include "image.h"
-void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, image *labels, int classes, int frame_skip);
+void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix);
#endif
diff --git a/src/detection_layer.c b/src/detection_layer.c
index 1fe67677de895295ea2f6a4d92e4e817496fb711..6ee7f648ac718b9f2ac5d5ac9d30f7664175b0d0 100644
--- a/src/detection_layer.c
+++ b/src/detection_layer.c
@@ -30,7 +30,12 @@ detection_layer make_detection_layer(int batch, int inputs, int n, int side, int
l.truths = l.side*l.side*(1+l.coords+l.classes);
l.output = calloc(batch*l.outputs, sizeof(float));
l.delta = calloc(batch*l.outputs, sizeof(float));
+
+ l.forward = forward_detection_layer;
+ l.backward = backward_detection_layer;
#ifdef GPU
+ l.forward_gpu = forward_detection_layer_gpu;
+ l.backward_gpu = backward_detection_layer_gpu;
l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
#endif
@@ -216,6 +221,35 @@ void backward_detection_layer(const detection_layer l, network_state state)
axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
}
+void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness)
+{
+ int i,j,n;
+ float *predictions = l.output;
+ //int per_cell = 5*num+classes;
+ for (i = 0; i < l.side*l.side; ++i){
+ int row = i / l.side;
+ int col = i % l.side;
+ for(n = 0; n < l.n; ++n){
+ int index = i*l.n + n;
+ int p_index = l.side*l.side*l.classes + i*l.n + n;
+ float scale = predictions[p_index];
+ int box_index = l.side*l.side*(l.classes + l.n) + (i*l.n + n)*4;
+ boxes[index].x = (predictions[box_index + 0] + col) / l.side * w;
+ boxes[index].y = (predictions[box_index + 1] + row) / l.side * h;
+ boxes[index].w = pow(predictions[box_index + 2], (l.sqrt?2:1)) * w;
+ boxes[index].h = pow(predictions[box_index + 3], (l.sqrt?2:1)) * h;
+ for(j = 0; j < l.classes; ++j){
+ int class_index = i*l.classes;
+ float prob = scale*predictions[class_index+j];
+ probs[index][j] = (prob > thresh) ? prob : 0;
+ }
+ if(only_objectness){
+ probs[index][0] = scale;
+ }
+ }
+ }
+}
+
#ifdef GPU
void forward_detection_layer_gpu(const detection_layer l, network_state state)
diff --git a/src/detection_layer.h b/src/detection_layer.h
index e8c3a725ae4b7d185d7924d48a278b817447465d..e847a094ccf355a194bf7e4859ba1f43831c4c0d 100644
--- a/src/detection_layer.h
+++ b/src/detection_layer.h
@@ -9,6 +9,7 @@ typedef layer detection_layer;
detection_layer make_detection_layer(int batch, int inputs, int n, int size, int classes, int coords, int rescore);
void forward_detection_layer(const detection_layer l, network_state state);
void backward_detection_layer(const detection_layer l, network_state state);
+void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness);
#ifdef GPU
void forward_detection_layer_gpu(const detection_layer l, network_state state);
diff --git a/src/detector.c b/src/detector.c
index 949875074c3bd6648aaa9f427d3cb5c41e2dff1a..1f48c61837c027b1835f030ce8279ea642cc0f7a 100644
--- a/src/detector.c
+++ b/src/detector.c
@@ -1,16 +1,16 @@
#include "network.h"
-#include "detection_layer.h"
+#include "region_layer.h"
#include "cost_layer.h"
#include "utils.h"
#include "parser.h"
#include "box.h"
+#include "demo.h"
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
static char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
-static image voc_labels[20];
void train_detector(char *cfgfile, char *weightfile)
{
@@ -49,13 +49,14 @@ void train_detector(char *cfgfile, char *weightfile)
args.num_boxes = l.max_boxes;
args.d = &buffer;
args.type = DETECTION_DATA;
+ args.threads = 4;
args.angle = net.angle;
args.exposure = net.exposure;
args.saturation = net.saturation;
args.hue = net.hue;
- pthread_t load_thread = load_data_in_thread(args);
+ pthread_t load_thread = load_data(args);
clock_t time;
//while(i*imgs < N*120){
while(get_current_batch(net) < net.max_batches){
@@ -63,7 +64,7 @@ void train_detector(char *cfgfile, char *weightfile)
time=clock();
pthread_join(load_thread, 0);
train = buffer;
- load_thread = load_data_in_thread(args);
+ load_thread = load_data(args);
/*
int k;
@@ -102,44 +103,6 @@ void train_detector(char *cfgfile, char *weightfile)
save_weights(net, buff);
}
-static void convert_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_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 index = i*num + n;
- int p_index = index * (classes + 5) + 4;
- float scale = predictions[p_index];
- int box_index = index * (classes + 5);
- boxes[index].x = (predictions[box_index + 0] + col + .5) / side * w;
- boxes[index].y = (predictions[box_index + 1] + row + .5) / side * h;
- if(0){
- boxes[index].x = (logistic_activate(predictions[box_index + 0]) + col) / side * w;
- boxes[index].y = (logistic_activate(predictions[box_index + 1]) + row) / side * h;
- }
- boxes[index].w = pow(logistic_activate(predictions[box_index + 2]), (square?2:1)) * w;
- boxes[index].h = pow(logistic_activate(predictions[box_index + 3]), (square?2:1)) * h;
- if(1){
- boxes[index].x = ((col + .5)/side + predictions[box_index + 0] * .5) * w;
- boxes[index].y = ((row + .5)/side + predictions[box_index + 1] * .5) * h;
- boxes[index].w = (exp(predictions[box_index + 2]) * .5) * w;
- boxes[index].h = (exp(predictions[box_index + 3]) * .5) * h;
- }
- for(j = 0; j < classes; ++j){
- int class_index = index * (classes + 5) + 5;
- float prob = scale*predictions[class_index+j];
- probs[index][j] = (prob > thresh) ? prob : 0;
- }
- if(only_objectness){
- probs[index][0] = scale;
- }
- }
- }
-}
-
void print_detector_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h)
{
int i, j;
@@ -179,7 +142,6 @@ void validate_detector(char *cfgfile, char *weightfile)
layer l = net.layers[net.n-1];
int classes = l.classes;
- int side = l.w;
int j;
FILE **fps = calloc(classes, sizeof(FILE *));
@@ -188,9 +150,9 @@ void validate_detector(char *cfgfile, char *weightfile)
snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
fps[j] = fopen(buff, "w");
}
- 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 *));
+ box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
+ float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
+ for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
int m = plist->size;
int i=0;
@@ -235,12 +197,12 @@ void validate_detector(char *cfgfile, char *weightfile)
char *path = paths[i+t-nthreads];
char *id = basecfg(path);
float *X = val_resized[t].data;
- float *predictions = network_predict(net, X);
+ network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
- convert_detections(predictions, classes, l.n, 0, side, w, h, thresh, probs, boxes, 0);
- if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, nms);
- print_detector_detections(fps, id, boxes, probs, side*side*l.n, classes, w, h);
+ get_region_boxes(l, w, h, thresh, probs, boxes, 0);
+ if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
+ print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h);
free(id);
free_image(val[t]);
free_image(val_resized[t]);
@@ -268,8 +230,6 @@ void validate_detector_recall(char *cfgfile, char *weightfile)
layer l = net.layers[net.n-1];
int classes = l.classes;
- int square = l.sqrt;
- int side = l.side;
int j, k;
FILE **fps = calloc(classes, sizeof(FILE *));
@@ -278,9 +238,9 @@ void validate_detector_recall(char *cfgfile, char *weightfile)
snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
fps[j] = fopen(buff, "w");
}
- 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 *));
+ box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
+ float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
+ for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
int m = plist->size;
int i=0;
@@ -299,18 +259,19 @@ void validate_detector_recall(char *cfgfile, char *weightfile)
image orig = load_image_color(path, 0, 0);
image sized = resize_image(orig, net.w, net.h);
char *id = basecfg(path);
- float *predictions = network_predict(net, sized.data);
- convert_detections(predictions, classes, l.n, square, l.w, 1, 1, thresh, probs, boxes, 1);
- if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms);
+ network_predict(net, sized.data);
+ get_region_boxes(l, 1, 1, thresh, probs, boxes, 1);
+ if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 1, nms);
- char *labelpath = find_replace(path, "images", "labels");
- labelpath = find_replace(labelpath, "JPEGImages", "labels");
- labelpath = find_replace(labelpath, ".jpg", ".txt");
- labelpath = find_replace(labelpath, ".JPEG", ".txt");
+ char labelpath[4096];
+ find_replace(path, "images", "labels", labelpath);
+ find_replace(labelpath, "JPEGImages", "labels", labelpath);
+ find_replace(labelpath, ".jpg", ".txt", labelpath);
+ find_replace(labelpath, ".JPEG", ".txt", labelpath);
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
- for(k = 0; k < side*side*l.n; ++k){
+ for(k = 0; k < l.w*l.h*l.n; ++k){
if(probs[k][0] > thresh){
++proposals;
}
@@ -319,7 +280,7 @@ void validate_detector_recall(char *cfgfile, char *weightfile)
++total;
box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};
float best_iou = 0;
- for(k = 0; k < side*side*l.n; ++k){
+ for(k = 0; k < l.w*l.h*l.n; ++k){
float iou = box_iou(boxes[k], t);
if(probs[k][0] > thresh && iou > best_iou){
best_iou = iou;
@@ -340,13 +301,12 @@ void validate_detector_recall(char *cfgfile, char *weightfile)
void test_detector(char *cfgfile, char *weightfile, char *filename, float thresh)
{
-
+ image *alphabet = load_alphabet();
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
}
- detection_layer l = net.layers[net.n-1];
- l.side = l.w;
+ layer l = net.layers[net.n-1];
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
@@ -354,9 +314,9 @@ void test_detector(char *cfgfile, char *weightfile, char *filename, float thresh
char *input = buff;
int j;
float nms=.4;
- box *boxes = calloc(l.side*l.side*l.n, sizeof(box));
- float **probs = calloc(l.side*l.side*l.n, sizeof(float *));
- for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));
+ box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
+ float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
+ for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));
while(1){
if(filename){
strncpy(input, filename, 256);
@@ -371,12 +331,12 @@ void test_detector(char *cfgfile, char *weightfile, char *filename, float thresh
image sized = resize_image(im, net.w, net.h);
float *X = sized.data;
time=clock();
- float *predictions = network_predict(net, X);
+ network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
- convert_detections(predictions, l.classes, l.n, 0, l.w, 1, 1, thresh, probs, boxes, 0);
- if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
- //draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, voc_labels, 20);
- draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, voc_labels, 20);
+ get_region_boxes(l, 1, 1, thresh, probs, boxes, 0);
+ if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);
+ //draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, voc_names, voc_labels, 20);
+ draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, voc_names, alphabet, 20);
save_image(im, "predictions");
show_image(im, "predictions");
@@ -392,14 +352,10 @@ void test_detector(char *cfgfile, char *weightfile, char *filename, float thresh
void run_detector(int argc, char **argv)
{
- int i;
- for(i = 0; i < 20; ++i){
- char buff[256];
- sprintf(buff, "data/labels/%s.png", voc_names[i]);
- voc_labels[i] = load_image_color(buff, 0, 0);
- }
-
+ char *prefix = find_char_arg(argc, argv, "-prefix", 0);
float thresh = find_float_arg(argc, argv, "-thresh", .2);
+ int cam_index = find_int_arg(argc, argv, "-c", 0);
+ int frame_skip = find_int_arg(argc, argv, "-s", 0);
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
@@ -412,4 +368,5 @@ void run_detector(int argc, char **argv)
else if(0==strcmp(argv[2], "train")) train_detector(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_detector(cfg, weights);
else if(0==strcmp(argv[2], "recall")) validate_detector_recall(cfg, weights);
+ else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, voc_names, 20, frame_skip, prefix);
}
diff --git a/src/dropout_layer.c b/src/dropout_layer.c
index 29b9193c1705a2ba33af8751968854cc3bcb3db1..82be64b1d80ff66742bc2c9374624c8fcb6cb5e3 100644
--- a/src/dropout_layer.c
+++ b/src/dropout_layer.c
@@ -15,7 +15,11 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability)
l.batch = batch;
l.rand = calloc(inputs*batch, sizeof(float));
l.scale = 1./(1.-probability);
+ l.forward = forward_dropout_layer;
+ l.backward = backward_dropout_layer;
#ifdef GPU
+ l.forward_gpu = forward_dropout_layer_gpu;
+ l.backward_gpu = backward_dropout_layer_gpu;
l.rand_gpu = cuda_make_array(l.rand, inputs*batch);
#endif
return l;
diff --git a/src/gru_layer.c b/src/gru_layer.c
index 4c720ce325c76273b9db5d72381ee5a83fd9672f..b78e86828d056dfcd60329f7d34ce6e59d29b201 100644
--- a/src/gru_layer.c
+++ b/src/gru_layer.c
@@ -85,7 +85,15 @@ layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_no
l.z_cpu = calloc(outputs*batch, sizeof(float));
l.h_cpu = calloc(outputs*batch, sizeof(float));
+ l.forward = forward_gru_layer;
+ l.backward = backward_gru_layer;
+ l.update = update_gru_layer;
+
#ifdef GPU
+ l.forward_gpu = forward_gru_layer_gpu;
+ l.backward_gpu = backward_gru_layer_gpu;
+ l.update_gpu = update_gru_layer_gpu;
+
l.forgot_state_gpu = cuda_make_array(l.output, batch*outputs);
l.forgot_delta_gpu = cuda_make_array(l.output, batch*outputs);
l.prev_state_gpu = cuda_make_array(l.output, batch*outputs);
diff --git a/src/gru_layer.h b/src/gru_layer.h
index bb9478b9e266c986f508261e52f7929cd5f8ec47..9e19cee1a68f6aa1f76f110a75bca2a7a8573e8f 100644
--- a/src/gru_layer.h
+++ b/src/gru_layer.h
@@ -1,24 +1,23 @@
-#ifndef RNN_LAYER_H
-#define RNN_LAYER_H
+#ifndef GRU_LAYER_H
+#define GRU_LAYER_H
#include "activations.h"
#include "layer.h"
#include "network.h"
-#define USET
-layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, ACTIVATION activation, int batch_normalize, int log);
+layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize);
-void forward_rnn_layer(layer l, network_state state);
-void backward_rnn_layer(layer l, network_state state);
-void update_rnn_layer(layer l, int batch, float learning_rate, float momentum, float decay);
+void forward_gru_layer(layer l, network_state state);
+void backward_gru_layer(layer l, network_state state);
+void update_gru_layer(layer l, int batch, float learning_rate, float momentum, float decay);
#ifdef GPU
-void forward_rnn_layer_gpu(layer l, network_state state);
-void backward_rnn_layer_gpu(layer l, network_state state);
-void update_rnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay);
-void push_rnn_layer(layer l);
-void pull_rnn_layer(layer l);
+void forward_gru_layer_gpu(layer l, network_state state);
+void backward_gru_layer_gpu(layer l, network_state state);
+void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay);
+void push_gru_layer(layer l);
+void pull_gru_layer(layer l);
#endif
#endif
diff --git a/src/image.c b/src/image.c
index 21c2f8bb62d0784cfcfb7302cce106b882dc6f98..09718fbe5fcd07bdf2aa3f930aab2b7bd271de2e 100644
--- a/src/image.c
+++ b/src/image.c
@@ -10,6 +10,12 @@
#define STB_IMAGE_WRITE_IMPLEMENTATION
#include "stb_image_write.h"
+#ifdef OPENCV
+#include "opencv2/highgui/highgui_c.h"
+#include "opencv2/imgproc/imgproc_c.h"
+#endif
+
+
int windows = 0;
float colors[6][3] = { {1,0,1}, {0,0,1},{0,1,1},{0,1,0},{1,1,0},{1,0,0} };
@@ -25,10 +31,66 @@ float get_color(int c, int x, int max)
return r;
}
+void composite_image(image source, image dest, int dx, int dy)
+{
+ int x,y,k;
+ for(k = 0; k < source.c; ++k){
+ for(y = 0; y < source.h; ++y){
+ for(x = 0; x < source.w; ++x){
+ float val = get_pixel(source, x, y, k);
+ float val2 = get_pixel_extend(dest, dx+x, dy+y, k);
+ set_pixel(dest, dx+x, dy+y, k, val * val2);
+ }
+ }
+ }
+}
+
+image border_image(image a, int border)
+{
+ image b = make_image(a.w + 2*border, a.h + 2*border, a.c);
+ int x,y,k;
+ for(k = 0; k < b.c; ++k){
+ for(y = 0; y < b.h; ++y){
+ for(x = 0; x < b.w; ++x){
+ float val = get_pixel_extend(a, x - border, y - border, k);
+ set_pixel(b, x, y, k, val);
+ }
+ }
+ }
+ return b;
+}
+
+image tile_images(image a, image b, int dx)
+{
+ if(a.w == 0) return copy_image(b);
+ image c = make_image(a.w + b.w + dx, (a.h > b.h) ? a.h : b.h, (a.c > b.c) ? a.c : b.c);
+ fill_cpu(c.w*c.h*c.c, 1, c.data, 1);
+ embed_image(a, c, 0, 0);
+ composite_image(b, c, a.w + dx, 0);
+ return c;
+}
+
+image get_label(image *characters, char *string)
+{
+ image label = make_empty_image(0,0,0);
+ while(*string){
+ image l = characters[(int)*string];
+ image n = tile_images(label, l, -4);
+ free_image(label);
+ label = n;
+ ++string;
+ }
+ image b = border_image(label, label.h*.25);
+ free_image(label);
+ return b;
+}
+
void draw_label(image a, int r, int c, image label, const float *rgb)
{
float ratio = (float) label.w / label.h;
- int h = label.h;
+ int h = a.h * .04;
+ h = label.h;
+ h = a.h * .06;
int w = ratio * h;
image rl = resize_image(label, w, h);
if (r - h >= 0) r = r - h;
@@ -102,7 +164,19 @@ void draw_bbox(image a, box bbox, int w, float r, float g, float b)
}
}
-void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image *labels, int classes)
+image *load_alphabet()
+{
+ int i;
+ image *alphabet = calloc(128, sizeof(image));
+ for(i = 32; i < 127; ++i){
+ char buff[256];
+ sprintf(buff, "data/labels/%d.png", i);
+ alphabet[i] = load_image_color(buff, 0, 0);
+ }
+ return alphabet;
+}
+
+void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image *alphabet, int classes)
{
int i;
@@ -111,7 +185,7 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs,
float prob = probs[i][class];
if(prob > thresh){
//int width = pow(prob, 1./2.)*30+1;
- int width = 8;
+ int width = im.h * .012;
printf("%s: %.0f%%\n", names[class], prob*100);
int offset = class*1 % classes;
float red = get_color(2,offset,classes);
@@ -137,7 +211,10 @@ void draw_detections(image im, int num, float thresh, box *boxes, float **probs,
if(bot > im.h-1) bot = im.h-1;
draw_box_width(im, left, top, right, bot, width, red, green, blue);
- if (labels) draw_label(im, top + width, left, labels[class], rgb);
+ if (alphabet) {
+ image label = get_label(alphabet, names[class]);
+ draw_label(im, top + width, left, label, rgb);
+ }
}
}
}
@@ -368,6 +445,53 @@ void show_image(image p, const char *name)
}
#ifdef OPENCV
+
+image ipl_to_image(IplImage* src)
+{
+ unsigned char *data = (unsigned char *)src->imageData;
+ int h = src->height;
+ int w = src->width;
+ int c = src->nChannels;
+ int step = src->widthStep;
+ image out = make_image(w, h, c);
+ int i, j, k, count=0;;
+
+ for(k= 0; k < c; ++k){
+ for(i = 0; i < h; ++i){
+ for(j = 0; j < w; ++j){
+ out.data[count++] = data[i*step + j*c + k]/255.;
+ }
+ }
+ }
+ return out;
+}
+
+image load_image_cv(char *filename, int channels)
+{
+ IplImage* src = 0;
+ int flag = -1;
+ if (channels == 0) flag = -1;
+ else if (channels == 1) flag = 0;
+ else if (channels == 3) flag = 1;
+ else {
+ fprintf(stderr, "OpenCV can't force load with %d channels\n", channels);
+ }
+
+ if( (src = cvLoadImage(filename, flag)) == 0 )
+ {
+ fprintf(stderr, "Cannot load image \"%s\"\n", filename);
+ char buff[256];
+ sprintf(buff, "echo %s >> bad.list", filename);
+ system(buff);
+ return make_image(10,10,3);
+ //exit(0);
+ }
+ image out = ipl_to_image(src);
+ cvReleaseImage(&src);
+ rgbgr_image(out);
+ return out;
+}
+
image get_image_from_stream(CvCapture *cap)
{
IplImage* src = cvQueryFrame(cap);
@@ -376,9 +500,7 @@ image get_image_from_stream(CvCapture *cap)
rgbgr_image(im);
return im;
}
-#endif
-#ifdef OPENCV
void save_image_jpg(image p, const char *name)
{
image copy = copy_image(p);
@@ -980,7 +1102,7 @@ void test_resize(char *filename)
image aug = random_augment_image(im, 0, 320, 448, 320, .75);
show_image(aug, "aug");
free_image(aug);
-
+
float exposure = 1.15;
float saturation = 1.15;
@@ -1001,55 +1123,6 @@ void test_resize(char *filename)
#endif
}
-#ifdef OPENCV
-image ipl_to_image(IplImage* src)
-{
- unsigned char *data = (unsigned char *)src->imageData;
- int h = src->height;
- int w = src->width;
- int c = src->nChannels;
- int step = src->widthStep;
- image out = make_image(w, h, c);
- int i, j, k, count=0;;
-
- for(k= 0; k < c; ++k){
- for(i = 0; i < h; ++i){
- for(j = 0; j < w; ++j){
- out.data[count++] = data[i*step + j*c + k]/255.;
- }
- }
- }
- return out;
-}
-
-image load_image_cv(char *filename, int channels)
-{
- IplImage* src = 0;
- int flag = -1;
- if (channels == 0) flag = -1;
- else if (channels == 1) flag = 0;
- else if (channels == 3) flag = 1;
- else {
- fprintf(stderr, "OpenCV can't force load with %d channels\n", channels);
- }
-
- if( (src = cvLoadImage(filename, flag)) == 0 )
- {
- fprintf(stderr, "Cannot load image \"%s\"\n", filename);
- char buff[256];
- sprintf(buff, "echo %s >> bad.list", filename);
- system(buff);
- return make_image(10,10,3);
- //exit(0);
- }
- image out = ipl_to_image(src);
- cvReleaseImage(&src);
- rgbgr_image(out);
- return out;
-}
-
-#endif
-
image load_image_stb(char *filename, int channels)
{
@@ -1122,6 +1195,7 @@ float get_pixel_extend(image m, int x, int y, int c)
}
void set_pixel(image m, int x, int y, int c, float val)
{
+ if (x < 0 || y < 0 || c < 0 || x >= m.w || y >= m.h || c >= m.c) return;
assert(x < m.w && y < m.h && c < m.c);
m.data[c*m.h*m.w + y*m.w + x] = val;
}
@@ -1247,5 +1321,7 @@ void show_images(image *ims, int n, char *window)
void free_image(image m)
{
- free(m.data);
+ if(m.data){
+ free(m.data);
+ }
}
diff --git a/src/image.h b/src/image.h
index e124860983706931580c34d5416a88756b9da1be..7e7ecf6db92303becf1702349488f72d28b032e1 100644
--- a/src/image.h
+++ b/src/image.h
@@ -8,11 +8,6 @@
#include <math.h>
#include "box.h"
-#ifdef OPENCV
-#include "opencv2/highgui/highgui_c.h"
-#include "opencv2/imgproc/imgproc_c.h"
-#endif
-
typedef struct {
int h;
int w;
@@ -26,6 +21,7 @@ void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b
void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b);
void draw_bbox(image a, box bbox, int w, float r, float g, float b);
void draw_label(image a, int r, int c, image label, const float *rgb);
+void write_label(image a, int r, int c, image *characters, char *string, float *rgb);
void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image *labels, int classes);
image image_distance(image a, image b);
void scale_image(image m, float s);
@@ -64,12 +60,6 @@ void show_images(image *ims, int n, char *window);
void show_image_layers(image p, char *name);
void show_image_collapsed(image p, char *name);
-#ifdef OPENCV
-void save_image_jpg(image p, const char *name);
-image get_image_from_stream(CvCapture *cap);
-image ipl_to_image(IplImage* src);
-#endif
-
void print_image(image m);
image make_image(int w, int h, int c);
@@ -79,6 +69,7 @@ image float_to_image(int w, int h, int c, float *data);
image copy_image(image p);
image load_image(char *filename, int w, int h, int c);
image load_image_color(char *filename, int w, int h);
+image *load_alphabet();
float get_pixel(image m, int x, int y, int c);
float get_pixel_extend(image m, int x, int y, int c);
diff --git a/src/layer.h b/src/layer.h
index 7dbbfb9fb5d03ce73b279322df366f93da08c1f6..ea6862b86a6cb916c443a63e7ee0cb670379d251 100644
--- a/src/layer.h
+++ b/src/layer.h
@@ -4,6 +4,8 @@
#include "activations.h"
#include "stddef.h"
+struct network_state;
+
struct layer;
typedef struct layer layer;
@@ -42,6 +44,12 @@ struct layer{
LAYER_TYPE type;
ACTIVATION activation;
COST_TYPE cost_type;
+ void (*forward) (struct layer, struct network_state);
+ void (*backward) (struct layer, struct network_state);
+ void (*update) (struct layer, int, float, float, float);
+ void (*forward_gpu) (struct layer, struct network_state);
+ void (*backward_gpu) (struct layer, struct network_state);
+ void (*update_gpu) (struct layer, int, float, float, float);
int batch_normalize;
int shortcut;
int batch;
diff --git a/src/local_layer.c b/src/local_layer.c
index 3696f8462f1f56c515494b2d408bc363c05fd3ec..31f0ca6ba56c2dced4a5ef9fc80e67ebbd33aa47 100644
--- a/src/local_layer.c
+++ b/src/local_layer.c
@@ -60,8 +60,16 @@ local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, in
l.col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float));
+
+ l.forward = forward_local_layer;
+ l.backward = backward_local_layer;
+ l.update = update_local_layer;
#ifdef GPU
+ l.forward_gpu = forward_local_layer_gpu;
+ l.backward_gpu = backward_local_layer_gpu;
+ l.update_gpu = update_local_layer_gpu;
+
l.weights_gpu = cuda_make_array(l.weights, c*n*size*size*locations);
l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size*locations);
diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c
index 3e0ea1561ab5c8c21a7241a48265824f7dd80525..49cfeaf50ae7c9ebb0681851f7d79b1ac32ced26 100644
--- a/src/maxpool_layer.c
+++ b/src/maxpool_layer.c
@@ -39,7 +39,11 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s
l.indexes = calloc(output_size, sizeof(int));
l.output = calloc(output_size, sizeof(float));
l.delta = calloc(output_size, sizeof(float));
+ l.forward = forward_maxpool_layer;
+ l.backward = backward_maxpool_layer;
#ifdef GPU
+ l.forward_gpu = forward_maxpool_layer_gpu;
+ l.backward_gpu = backward_maxpool_layer_gpu;
l.indexes_gpu = cuda_make_int_array(output_size);
l.output_gpu = cuda_make_array(l.output, output_size);
l.delta_gpu = cuda_make_array(l.delta, output_size);
diff --git a/src/network.c b/src/network.c
index 72c89432b13ed7e351d4d0828af7e8ee647c7f6f..01b796222b25c5d077ee5e2f039545d26574950f 100644
--- a/src/network.c
+++ b/src/network.c
@@ -15,7 +15,6 @@
#include "local_layer.h"
#include "convolutional_layer.h"
#include "activation_layer.h"
-#include "deconvolutional_layer.h"
#include "detection_layer.h"
#include "region_layer.h"
#include "normalization_layer.h"
@@ -153,49 +152,7 @@ void forward_network(network net, network_state state)
if(l.delta){
scal_cpu(l.outputs * l.batch, 0, l.delta, 1);
}
- if(l.type == CONVOLUTIONAL){
- forward_convolutional_layer(l, state);
- } else if(l.type == DECONVOLUTIONAL){
- forward_deconvolutional_layer(l, state);
- } else if(l.type == ACTIVE){
- forward_activation_layer(l, state);
- } else if(l.type == LOCAL){
- forward_local_layer(l, state);
- } else if(l.type == NORMALIZATION){
- forward_normalization_layer(l, state);
- } else if(l.type == BATCHNORM){
- forward_batchnorm_layer(l, state);
- } else if(l.type == DETECTION){
- forward_detection_layer(l, state);
- } else if(l.type == REGION){
- forward_region_layer(l, state);
- } else if(l.type == CONNECTED){
- forward_connected_layer(l, state);
- } else if(l.type == RNN){
- forward_rnn_layer(l, state);
- } else if(l.type == GRU){
- forward_gru_layer(l, state);
- } else if(l.type == CRNN){
- forward_crnn_layer(l, state);
- } else if(l.type == CROP){
- forward_crop_layer(l, state);
- } else if(l.type == COST){
- forward_cost_layer(l, state);
- } else if(l.type == SOFTMAX){
- forward_softmax_layer(l, state);
- } else if(l.type == MAXPOOL){
- forward_maxpool_layer(l, state);
- } else if(l.type == REORG){
- forward_reorg_layer(l, state);
- } else if(l.type == AVGPOOL){
- forward_avgpool_layer(l, state);
- } else if(l.type == DROPOUT){
- forward_dropout_layer(l, state);
- } else if(l.type == ROUTE){
- forward_route_layer(l, net);
- } else if(l.type == SHORTCUT){
- forward_shortcut_layer(l, state);
- }
+ l.forward(l, state);
state.input = l.output;
}
}
@@ -207,29 +164,17 @@ void update_network(network net)
float rate = get_current_rate(net);
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
- if(l.type == CONVOLUTIONAL){
- update_convolutional_layer(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == DECONVOLUTIONAL){
- update_deconvolutional_layer(l, rate, net.momentum, net.decay);
- } else if(l.type == CONNECTED){
- update_connected_layer(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == RNN){
- update_rnn_layer(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == GRU){
- update_gru_layer(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == CRNN){
- update_crnn_layer(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == LOCAL){
- update_local_layer(l, update_batch, rate, net.momentum, net.decay);
+ if(l.update){
+ l.update(l, update_batch, rate, net.momentum, net.decay);
}
}
}
float *get_network_output(network net)
{
- #ifdef GPU
- if (gpu_index >= 0) return get_network_output_gpu(net);
- #endif
+#ifdef GPU
+ if (gpu_index >= 0) return get_network_output_gpu(net);
+#endif
int i;
for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
return net.layers[i].output;
@@ -273,47 +218,7 @@ void backward_network(network net, network_state state)
state.delta = prev.delta;
}
layer l = net.layers[i];
- if(l.type == CONVOLUTIONAL){
- backward_convolutional_layer(l, state);
- } else if(l.type == DECONVOLUTIONAL){
- backward_deconvolutional_layer(l, state);
- } else if(l.type == ACTIVE){
- backward_activation_layer(l, state);
- } else if(l.type == NORMALIZATION){
- backward_normalization_layer(l, state);
- } else if(l.type == BATCHNORM){
- backward_batchnorm_layer(l, state);
- } else if(l.type == MAXPOOL){
- if(i != 0) backward_maxpool_layer(l, state);
- } else if(l.type == REORG){
- backward_reorg_layer(l, state);
- } else if(l.type == AVGPOOL){
- backward_avgpool_layer(l, state);
- } else if(l.type == DROPOUT){
- backward_dropout_layer(l, state);
- } else if(l.type == DETECTION){
- backward_detection_layer(l, state);
- } else if(l.type == REGION){
- backward_region_layer(l, state);
- } else if(l.type == SOFTMAX){
- if(i != 0) backward_softmax_layer(l, state);
- } else if(l.type == CONNECTED){
- backward_connected_layer(l, state);
- } else if(l.type == RNN){
- backward_rnn_layer(l, state);
- } else if(l.type == GRU){
- backward_gru_layer(l, state);
- } else if(l.type == CRNN){
- backward_crnn_layer(l, state);
- } else if(l.type == LOCAL){
- backward_local_layer(l, state);
- } else if(l.type == COST){
- backward_cost_layer(l, state);
- } else if(l.type == ROUTE){
- backward_route_layer(l, net);
- } else if(l.type == SHORTCUT){
- backward_shortcut_layer(l, state);
- }
+ l.backward(l, state);
}
}
@@ -406,11 +311,11 @@ void set_batch_network(network *net, int b)
int i;
for(i = 0; i < net->n; ++i){
net->layers[i].batch = b;
- #ifdef CUDNN
+#ifdef CUDNN
if(net->layers[i].type == CONVOLUTIONAL){
cudnn_convolutional_setup(net->layers + i);
}
- #endif
+#endif
}
}
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index b7d1d2b70ca180bf1dd320003f7a63e0c1de759e..e31906807adbe57f3e4011170aa980b035e92ecd 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -22,7 +22,6 @@ extern "C" {
#include "region_layer.h"
#include "convolutional_layer.h"
#include "activation_layer.h"
-#include "deconvolutional_layer.h"
#include "maxpool_layer.h"
#include "reorg_layer.h"
#include "avgpool_layer.h"
@@ -51,49 +50,7 @@ void forward_network_gpu(network net, network_state state)
if(l.delta_gpu){
fill_ongpu(l.outputs * l.batch, 0, l.delta_gpu, 1);
}
- if(l.type == CONVOLUTIONAL){
- forward_convolutional_layer_gpu(l, state);
- } else if(l.type == DECONVOLUTIONAL){
- forward_deconvolutional_layer_gpu(l, state);
- } else if(l.type == ACTIVE){
- forward_activation_layer_gpu(l, state);
- } else if(l.type == LOCAL){
- forward_local_layer_gpu(l, state);
- } else if(l.type == DETECTION){
- forward_detection_layer_gpu(l, state);
- } else if(l.type == REGION){
- forward_region_layer_gpu(l, state);
- } else if(l.type == CONNECTED){
- forward_connected_layer_gpu(l, state);
- } else if(l.type == RNN){
- forward_rnn_layer_gpu(l, state);
- } else if(l.type == GRU){
- forward_gru_layer_gpu(l, state);
- } else if(l.type == CRNN){
- forward_crnn_layer_gpu(l, state);
- } else if(l.type == CROP){
- forward_crop_layer_gpu(l, state);
- } else if(l.type == COST){
- forward_cost_layer_gpu(l, state);
- } else if(l.type == SOFTMAX){
- forward_softmax_layer_gpu(l, state);
- } else if(l.type == NORMALIZATION){
- forward_normalization_layer_gpu(l, state);
- } else if(l.type == BATCHNORM){
- forward_batchnorm_layer_gpu(l, state);
- } else if(l.type == MAXPOOL){
- forward_maxpool_layer_gpu(l, state);
- } else if(l.type == REORG){
- forward_reorg_layer_gpu(l, state);
- } else if(l.type == AVGPOOL){
- forward_avgpool_layer_gpu(l, state);
- } else if(l.type == DROPOUT){
- forward_dropout_layer_gpu(l, state);
- } else if(l.type == ROUTE){
- forward_route_layer_gpu(l, net);
- } else if(l.type == SHORTCUT){
- forward_shortcut_layer_gpu(l, state);
- }
+ l.forward_gpu(l, state);
state.input = l.output_gpu;
}
}
@@ -115,47 +72,7 @@ void backward_network_gpu(network net, network_state state)
state.input = prev.output_gpu;
state.delta = prev.delta_gpu;
}
- if(l.type == CONVOLUTIONAL){
- backward_convolutional_layer_gpu(l, state);
- } else if(l.type == DECONVOLUTIONAL){
- backward_deconvolutional_layer_gpu(l, state);
- } else if(l.type == ACTIVE){
- backward_activation_layer_gpu(l, state);
- } else if(l.type == LOCAL){
- backward_local_layer_gpu(l, state);
- } else if(l.type == MAXPOOL){
- if(i != 0) backward_maxpool_layer_gpu(l, state);
- } else if(l.type == REORG){
- backward_reorg_layer_gpu(l, state);
- } else if(l.type == AVGPOOL){
- if(i != 0) backward_avgpool_layer_gpu(l, state);
- } else if(l.type == DROPOUT){
- backward_dropout_layer_gpu(l, state);
- } else if(l.type == DETECTION){
- backward_detection_layer_gpu(l, state);
- } else if(l.type == REGION){
- backward_region_layer_gpu(l, state);
- } else if(l.type == NORMALIZATION){
- backward_normalization_layer_gpu(l, state);
- } else if(l.type == BATCHNORM){
- backward_batchnorm_layer_gpu(l, state);
- } else if(l.type == SOFTMAX){
- if(i != 0) backward_softmax_layer_gpu(l, state);
- } else if(l.type == CONNECTED){
- backward_connected_layer_gpu(l, state);
- } else if(l.type == RNN){
- backward_rnn_layer_gpu(l, state);
- } else if(l.type == GRU){
- backward_gru_layer_gpu(l, state);
- } else if(l.type == CRNN){
- backward_crnn_layer_gpu(l, state);
- } else if(l.type == COST){
- backward_cost_layer_gpu(l, state);
- } else if(l.type == ROUTE){
- backward_route_layer_gpu(l, net);
- } else if(l.type == SHORTCUT){
- backward_shortcut_layer_gpu(l, state);
- }
+ l.backward_gpu(l, state);
}
}
@@ -166,20 +83,8 @@ void update_network_gpu(network net)
float rate = get_current_rate(net);
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
- if(l.type == CONVOLUTIONAL){
- update_convolutional_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == DECONVOLUTIONAL){
- update_deconvolutional_layer_gpu(l, rate, net.momentum, net.decay);
- } else if(l.type == CONNECTED){
- update_connected_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == GRU){
- update_gru_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == RNN){
- update_rnn_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == CRNN){
- update_crnn_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == LOCAL){
- update_local_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
+ if(l.update_gpu){
+ l.update_gpu(l, update_batch, rate, net.momentum, net.decay);
}
}
}
@@ -271,20 +176,8 @@ void update_layer(layer l, network net)
{
int update_batch = net.batch*net.subdivisions;
float rate = get_current_rate(net);
- if(l.type == CONVOLUTIONAL){
- update_convolutional_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == DECONVOLUTIONAL){
- update_deconvolutional_layer_gpu(l, rate, net.momentum, net.decay);
- } else if(l.type == CONNECTED){
- update_connected_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == RNN){
- update_rnn_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == GRU){
- update_gru_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == CRNN){
- update_crnn_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
- } else if(l.type == LOCAL){
- update_local_layer_gpu(l, update_batch, rate, net.momentum, net.decay);
+ if(l.update_gpu){
+ l.update_gpu(l, update_batch, rate, net.momentum, net.decay);
}
}
@@ -463,7 +356,7 @@ float train_networks(network *nets, int n, data d, int interval)
}
for(i = 0; i < n; ++i){
pthread_join(threads[i], 0);
- printf("%f\n", errors[i]);
+ //printf("%f\n", errors[i]);
sum += errors[i];
}
if (get_current_batch(nets[0]) % interval == 0) {
@@ -492,6 +385,7 @@ float *get_network_output_gpu(network net)
float *network_predict_gpu(network net, float *input)
{
+ cuda_set_device(net.gpu_index);
int size = get_network_input_size(net) * net.batch;
network_state state;
state.index = 0;
diff --git a/src/normalization_layer.c b/src/normalization_layer.c
index 0551337a49245c5669723228910d19c2c4638edb..069a07924902498c7e40d12add08a2d881ac0c7d 100644
--- a/src/normalization_layer.c
+++ b/src/normalization_layer.c
@@ -21,7 +21,13 @@ layer make_normalization_layer(int batch, int w, int h, int c, int size, float a
layer.norms = calloc(h * w * c * batch, sizeof(float));
layer.inputs = w*h*c;
layer.outputs = layer.inputs;
+
+ layer.forward = forward_normalization_layer;
+ layer.backward = backward_normalization_layer;
#ifdef GPU
+ layer.forward_gpu = forward_normalization_layer_gpu;
+ layer.backward_gpu = backward_normalization_layer_gpu;
+
layer.output_gpu = cuda_make_array(layer.output, h * w * c * batch);
layer.delta_gpu = cuda_make_array(layer.delta, h * w * c * batch);
layer.squared_gpu = cuda_make_array(layer.squared, h * w * c * batch);
diff --git a/src/parser.c b/src/parser.c
index 2b285b51a9821f128df613b18fa81bd57395d6f2..a27d245998133024cbbaeaea9364b5434c937ff4 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -12,7 +12,6 @@
#include "activation_layer.h"
#include "normalization_layer.h"
#include "batchnorm_layer.h"
-#include "deconvolutional_layer.h"
#include "connected_layer.h"
#include "rnn_layer.h"
#include "gru_layer.h"
@@ -36,30 +35,42 @@ typedef struct{
list *options;
}section;
-int is_network(section *s);
-int is_convolutional(section *s);
-int is_activation(section *s);
-int is_local(section *s);
-int is_deconvolutional(section *s);
-int is_connected(section *s);
-int is_rnn(section *s);
-int is_gru(section *s);
-int is_crnn(section *s);
-int is_maxpool(section *s);
-int is_reorg(section *s);
-int is_avgpool(section *s);
-int is_dropout(section *s);
-int is_softmax(section *s);
-int is_normalization(section *s);
-int is_batchnorm(section *s);
-int is_crop(section *s);
-int is_shortcut(section *s);
-int is_cost(section *s);
-int is_detection(section *s);
-int is_region(section *s);
-int is_route(section *s);
list *read_cfg(char *filename);
+LAYER_TYPE string_to_layer_type(char * type)
+{
+
+ if (strcmp(type, "[shortcut]")==0) return SHORTCUT;
+ if (strcmp(type, "[crop]")==0) return CROP;
+ if (strcmp(type, "[cost]")==0) return COST;
+ if (strcmp(type, "[detection]")==0) return DETECTION;
+ if (strcmp(type, "[region]")==0) return REGION;
+ if (strcmp(type, "[local]")==0) return LOCAL;
+ if (strcmp(type, "[conv]")==0
+ || strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL;
+ if (strcmp(type, "[activation]")==0) return ACTIVE;
+ if (strcmp(type, "[net]")==0
+ || strcmp(type, "[network]")==0) return NETWORK;
+ if (strcmp(type, "[crnn]")==0) return CRNN;
+ if (strcmp(type, "[gru]")==0) return GRU;
+ if (strcmp(type, "[rnn]")==0) return RNN;
+ if (strcmp(type, "[conn]")==0
+ || strcmp(type, "[connected]")==0) return CONNECTED;
+ if (strcmp(type, "[max]")==0
+ || strcmp(type, "[maxpool]")==0) return MAXPOOL;
+ if (strcmp(type, "[reorg]")==0) return REORG;
+ if (strcmp(type, "[avg]")==0
+ || strcmp(type, "[avgpool]")==0) return AVGPOOL;
+ if (strcmp(type, "[dropout]")==0) return DROPOUT;
+ if (strcmp(type, "[lrn]")==0
+ || strcmp(type, "[normalization]")==0) return NORMALIZATION;
+ if (strcmp(type, "[batchnorm]")==0) return BATCHNORM;
+ if (strcmp(type, "[soft]")==0
+ || strcmp(type, "[softmax]")==0) return SOFTMAX;
+ if (strcmp(type, "[route]")==0) return ROUTE;
+ return BLANK;
+}
+
void free_section(section *s)
{
free(s->type);
@@ -102,26 +113,6 @@ typedef struct size_params{
int time_steps;
} size_params;
-deconvolutional_layer parse_deconvolutional(list *options, size_params params)
-{
- int n = option_find_int(options, "filters",1);
- int size = option_find_int(options, "size",1);
- int stride = option_find_int(options, "stride",1);
- char *activation_s = option_find_str(options, "activation", "logistic");
- ACTIVATION activation = get_activation(activation_s);
-
- int batch,h,w,c;
- h = params.h;
- w = params.w;
- c = params.c;
- batch=params.batch;
- if(!(h && w && c)) error("Layer before deconvolutional layer must output image.");
-
- deconvolutional_layer layer = make_deconvolutional_layer(batch,h,w,c,n,size,stride,activation);
-
- return layer;
-}
-
local_layer parse_local(list *options, size_params params)
{
int n = option_find_int(options, "filters",1);
@@ -545,6 +536,12 @@ void parse_net_options(list *options, network *net)
net->max_batches = option_find_int(options, "max_batches", 0);
}
+int is_network(section *s)
+{
+ return (strcmp(s->type, "[net]")==0
+ || strcmp(s->type, "[network]")==0);
+}
+
network parse_network_cfg(char *filename)
{
list *sections = read_cfg(filename);
@@ -576,47 +573,46 @@ network parse_network_cfg(char *filename)
s = (section *)n->val;
options = s->options;
layer l = {0};
- if(is_convolutional(s)){
+ LAYER_TYPE lt = string_to_layer_type(s->type);
+ if(lt == CONVOLUTIONAL){
l = parse_convolutional(options, params);
- }else if(is_local(s)){
+ }else if(lt == LOCAL){
l = parse_local(options, params);
- }else if(is_activation(s)){
+ }else if(lt == ACTIVE){
l = parse_activation(options, params);
- }else if(is_deconvolutional(s)){
- l = parse_deconvolutional(options, params);
- }else if(is_rnn(s)){
+ }else if(lt == RNN){
l = parse_rnn(options, params);
- }else if(is_gru(s)){
+ }else if(lt == GRU){
l = parse_gru(options, params);
- }else if(is_crnn(s)){
+ }else if(lt == CRNN){
l = parse_crnn(options, params);
- }else if(is_connected(s)){
+ }else if(lt == CONNECTED){
l = parse_connected(options, params);
- }else if(is_crop(s)){
+ }else if(lt == CROP){
l = parse_crop(options, params);
- }else if(is_cost(s)){
+ }else if(lt == COST){
l = parse_cost(options, params);
- }else if(is_region(s)){
+ }else if(lt == REGION){
l = parse_region(options, params);
- }else if(is_detection(s)){
+ }else if(lt == DETECTION){
l = parse_detection(options, params);
- }else if(is_softmax(s)){
+ }else if(lt == SOFTMAX){
l = parse_softmax(options, params);
- }else if(is_normalization(s)){
+ }else if(lt == NORMALIZATION){
l = parse_normalization(options, params);
- }else if(is_batchnorm(s)){
+ }else if(lt == BATCHNORM){
l = parse_batchnorm(options, params);
- }else if(is_maxpool(s)){
+ }else if(lt == MAXPOOL){
l = parse_maxpool(options, params);
- }else if(is_reorg(s)){
+ }else if(lt == REORG){
l = parse_reorg(options, params);
- }else if(is_avgpool(s)){
+ }else if(lt == AVGPOOL){
l = parse_avgpool(options, params);
- }else if(is_route(s)){
+ }else if(lt == ROUTE){
l = parse_route(options, params, net);
- }else if(is_shortcut(s)){
+ }else if(lt == SHORTCUT){
l = parse_shortcut(options, params, net);
- }else if(is_dropout(s)){
+ }else if(lt == DROPOUT){
l = parse_dropout(options, params);
l.output = net.layers[count-1].output;
l.delta = net.layers[count-1].delta;
@@ -660,142 +656,6 @@ network parse_network_cfg(char *filename)
return net;
}
-LAYER_TYPE string_to_layer_type(char * type)
-{
-
- if (strcmp(type, "[shortcut]")==0) return SHORTCUT;
- if (strcmp(type, "[crop]")==0) return CROP;
- if (strcmp(type, "[cost]")==0) return COST;
- if (strcmp(type, "[detection]")==0) return DETECTION;
- if (strcmp(type, "[region]")==0) return REGION;
- if (strcmp(type, "[local]")==0) return LOCAL;
- if (strcmp(type, "[deconv]")==0
- || strcmp(type, "[deconvolutional]")==0) return DECONVOLUTIONAL;
- if (strcmp(type, "[conv]")==0
- || strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL;
- if (strcmp(type, "[activation]")==0) return ACTIVE;
- if (strcmp(type, "[net]")==0
- || strcmp(type, "[network]")==0) return NETWORK;
- if (strcmp(type, "[crnn]")==0) return CRNN;
- if (strcmp(type, "[gru]")==0) return GRU;
- if (strcmp(type, "[rnn]")==0) return RNN;
- if (strcmp(type, "[conn]")==0
- || strcmp(type, "[connected]")==0) return CONNECTED;
- if (strcmp(type, "[max]")==0
- || strcmp(type, "[maxpool]")==0) return MAXPOOL;
- if (strcmp(type, "[reorg]")==0) return REORG;
- if (strcmp(type, "[avg]")==0
- || strcmp(type, "[avgpool]")==0) return AVGPOOL;
- if (strcmp(type, "[dropout]")==0) return DROPOUT;
- if (strcmp(type, "[lrn]")==0
- || strcmp(type, "[normalization]")==0) return NORMALIZATION;
- if (strcmp(type, "[batchnorm]")==0) return BATCHNORM;
- if (strcmp(type, "[soft]")==0
- || strcmp(type, "[softmax]")==0) return SOFTMAX;
- if (strcmp(type, "[route]")==0) return ROUTE;
- return BLANK;
-}
-
-int is_shortcut(section *s)
-{
- return (strcmp(s->type, "[shortcut]")==0);
-}
-int is_crop(section *s)
-{
- return (strcmp(s->type, "[crop]")==0);
-}
-int is_cost(section *s)
-{
- return (strcmp(s->type, "[cost]")==0);
-}
-int is_region(section *s)
-{
- return (strcmp(s->type, "[region]")==0);
-}
-int is_detection(section *s)
-{
- return (strcmp(s->type, "[detection]")==0);
-}
-int is_local(section *s)
-{
- return (strcmp(s->type, "[local]")==0);
-}
-int is_deconvolutional(section *s)
-{
- return (strcmp(s->type, "[deconv]")==0
- || strcmp(s->type, "[deconvolutional]")==0);
-}
-int is_convolutional(section *s)
-{
- return (strcmp(s->type, "[conv]")==0
- || strcmp(s->type, "[convolutional]")==0);
-}
-int is_activation(section *s)
-{
- return (strcmp(s->type, "[activation]")==0);
-}
-int is_network(section *s)
-{
- return (strcmp(s->type, "[net]")==0
- || strcmp(s->type, "[network]")==0);
-}
-int is_crnn(section *s)
-{
- return (strcmp(s->type, "[crnn]")==0);
-}
-int is_gru(section *s)
-{
- return (strcmp(s->type, "[gru]")==0);
-}
-int is_rnn(section *s)
-{
- return (strcmp(s->type, "[rnn]")==0);
-}
-int is_connected(section *s)
-{
- return (strcmp(s->type, "[conn]")==0
- || strcmp(s->type, "[connected]")==0);
-}
-int is_reorg(section *s)
-{
- return (strcmp(s->type, "[reorg]")==0);
-}
-int is_maxpool(section *s)
-{
- return (strcmp(s->type, "[max]")==0
- || strcmp(s->type, "[maxpool]")==0);
-}
-int is_avgpool(section *s)
-{
- return (strcmp(s->type, "[avg]")==0
- || strcmp(s->type, "[avgpool]")==0);
-}
-int is_dropout(section *s)
-{
- return (strcmp(s->type, "[dropout]")==0);
-}
-
-int is_normalization(section *s)
-{
- return (strcmp(s->type, "[lrn]")==0
- || strcmp(s->type, "[normalization]")==0);
-}
-
-int is_batchnorm(section *s)
-{
- return (strcmp(s->type, "[batchnorm]")==0);
-}
-
-int is_softmax(section *s)
-{
- return (strcmp(s->type, "[soft]")==0
- || strcmp(s->type, "[softmax]")==0);
-}
-int is_route(section *s)
-{
- return (strcmp(s->type, "[route]")==0);
-}
-
list *read_cfg(char *filename)
{
FILE *file = fopen(filename, "r");
@@ -831,45 +691,6 @@ list *read_cfg(char *filename)
return sections;
}
-void save_weights_double(network net, char *filename)
-{
- fprintf(stderr, "Saving doubled weights to %s\n", filename);
- FILE *fp = fopen(filename, "w");
- if(!fp) file_error(filename);
-
- fwrite(&net.learning_rate, sizeof(float), 1, fp);
- fwrite(&net.momentum, sizeof(float), 1, fp);
- fwrite(&net.decay, sizeof(float), 1, fp);
- fwrite(net.seen, sizeof(int), 1, fp);
-
- int i,j,k;
- for(i = 0; i < net.n; ++i){
- layer l = net.layers[i];
- if(l.type == CONVOLUTIONAL){
-#ifdef GPU
- if(gpu_index >= 0){
- pull_convolutional_layer(l);
- }
-#endif
- float zero = 0;
- fwrite(l.biases, sizeof(float), l.n, fp);
- fwrite(l.biases, sizeof(float), l.n, fp);
-
- for (j = 0; j < l.n; ++j){
- int index = j*l.c*l.size*l.size;
- fwrite(l.weights+index, sizeof(float), l.c*l.size*l.size, fp);
- for (k = 0; k < l.c*l.size*l.size; ++k) fwrite(&zero, sizeof(float), 1, fp);
- }
- for (j = 0; j < l.n; ++j){
- int index = j*l.c*l.size*l.size;
- for (k = 0; k < l.c*l.size*l.size; ++k) fwrite(&zero, sizeof(float), 1, fp);
- fwrite(l.weights+index, sizeof(float), l.c*l.size*l.size, fp);
- }
- }
- }
- fclose(fp);
-}
-
void save_convolutional_weights_binary(layer l, FILE *fp)
{
#ifdef GPU
@@ -1147,16 +968,6 @@ void load_weights_upto(network *net, char *filename, int cutoff)
if(l.type == CONVOLUTIONAL){
load_convolutional_weights(l, fp);
}
- if(l.type == DECONVOLUTIONAL){
- int num = l.n*l.c*l.size*l.size;
- fread(l.biases, sizeof(float), l.n, fp);
- fread(l.weights, sizeof(float), num, fp);
-#ifdef GPU
- if(gpu_index >= 0){
- push_deconvolutional_layer(l);
- }
-#endif
- }
if(l.type == CONNECTED){
load_connected_weights(l, fp, transpose);
}
diff --git a/src/region_layer.c b/src/region_layer.c
index 24d31690548e9af85094590fffeb523ced453452..bc3acaaec914aadb0ffc93175db522810f3cfc21 100644
--- a/src/region_layer.c
+++ b/src/region_layer.c
@@ -34,7 +34,11 @@ region_layer make_region_layer(int batch, int w, int h, int n, int classes, int
l.biases[i] = .5;
}
+ l.forward = forward_region_layer;
+ l.backward = backward_region_layer;
#ifdef GPU
+ l.forward_gpu = forward_region_layer_gpu;
+ l.backward_gpu = backward_region_layer_gpu;
l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
#endif
@@ -228,6 +232,45 @@ void backward_region_layer(const region_layer l, network_state state)
axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
}
+void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness)
+{
+ int i,j,n;
+ float *predictions = l.output;
+ //int per_cell = 5*num+classes;
+ for (i = 0; i < l.w*l.h; ++i){
+ int row = i / l.w;
+ int col = i % l.w;
+ for(n = 0; n < l.n; ++n){
+ int index = i*l.n + n;
+ int p_index = index * (l.classes + 5) + 4;
+ float scale = predictions[p_index];
+ int box_index = index * (l.classes + 5);
+ boxes[index].x = (predictions[box_index + 0] + col + .5) / l.w * w;
+ boxes[index].y = (predictions[box_index + 1] + row + .5) / l.h * h;
+ if(0){
+ boxes[index].x = (logistic_activate(predictions[box_index + 0]) + col) / l.w * w;
+ boxes[index].y = (logistic_activate(predictions[box_index + 1]) + row) / l.h * h;
+ }
+ boxes[index].w = pow(logistic_activate(predictions[box_index + 2]), (l.sqrt?2:1)) * w;
+ boxes[index].h = pow(logistic_activate(predictions[box_index + 3]), (l.sqrt?2:1)) * h;
+ if(1){
+ boxes[index].x = ((col + .5)/l.w + predictions[box_index + 0] * .5) * w;
+ boxes[index].y = ((row + .5)/l.h + predictions[box_index + 1] * .5) * h;
+ boxes[index].w = (exp(predictions[box_index + 2]) * .5) * w;
+ boxes[index].h = (exp(predictions[box_index + 3]) * .5) * h;
+ }
+ for(j = 0; j < l.classes; ++j){
+ int class_index = index * (l.classes + 5) + 5;
+ float prob = scale*predictions[class_index+j];
+ probs[index][j] = (prob > thresh) ? prob : 0;
+ }
+ if(only_objectness){
+ probs[index][0] = scale;
+ }
+ }
+ }
+}
+
#ifdef GPU
void forward_region_layer_gpu(const region_layer l, network_state state)
diff --git a/src/region_layer.h b/src/region_layer.h
index a4156fd06e3c5d3bc0660c53e62bc906e65d4210..01901e076a6123b9dc7f48da6dd2b115a9aa1145 100644
--- a/src/region_layer.h
+++ b/src/region_layer.h
@@ -9,6 +9,7 @@ typedef layer region_layer;
region_layer make_region_layer(int batch, int h, int w, int n, int classes, int coords);
void forward_region_layer(const region_layer l, network_state state);
void backward_region_layer(const region_layer l, network_state state);
+void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness);
#ifdef GPU
void forward_region_layer_gpu(const region_layer l, network_state state);
diff --git a/src/reorg_layer.c b/src/reorg_layer.c
index 55b425f1a57c18c12f94b5987263b0544dadf838..5bc257a377c160e9a83e83bb3f07a299836dd4c1 100644
--- a/src/reorg_layer.c
+++ b/src/reorg_layer.c
@@ -22,7 +22,13 @@ layer make_reorg_layer(int batch, int h, int w, int c, int stride)
int output_size = l.out_h * l.out_w * l.out_c * batch;
l.output = calloc(output_size, sizeof(float));
l.delta = calloc(output_size, sizeof(float));
+
+ l.forward = forward_reorg_layer;
+ l.backward = backward_reorg_layer;
#ifdef GPU
+ l.forward_gpu = forward_reorg_layer_gpu;
+ l.backward_gpu = backward_reorg_layer_gpu;
+
l.output_gpu = cuda_make_array(l.output, output_size);
l.delta_gpu = cuda_make_array(l.delta, output_size);
#endif
diff --git a/src/rnn_layer.c b/src/rnn_layer.c
index b713899cf49376deed20434b81138f4de715642a..83fda13e6fe48187e4f8fec681f2ed4fa91c8883 100644
--- a/src/rnn_layer.c
+++ b/src/rnn_layer.c
@@ -58,7 +58,13 @@ layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps,
l.output = l.output_layer->output;
l.delta = l.output_layer->delta;
+ l.forward = forward_rnn_layer;
+ l.backward = backward_rnn_layer;
+ l.update = update_rnn_layer;
#ifdef GPU
+ l.forward_gpu = forward_rnn_layer_gpu;
+ l.backward_gpu = backward_rnn_layer_gpu;
+ l.update_gpu = update_rnn_layer_gpu;
l.state_gpu = cuda_make_array(l.state, batch*hidden*(steps+1));
l.output_gpu = l.output_layer->output_gpu;
l.delta_gpu = l.output_layer->delta_gpu;
diff --git a/src/rnn_layer.h b/src/rnn_layer.h
index 9e19cee1a68f6aa1f76f110a75bca2a7a8573e8f..bb9478b9e266c986f508261e52f7929cd5f8ec47 100644
--- a/src/rnn_layer.h
+++ b/src/rnn_layer.h
@@ -1,23 +1,24 @@
-#ifndef GRU_LAYER_H
-#define GRU_LAYER_H
+#ifndef RNN_LAYER_H
+#define RNN_LAYER_H
#include "activations.h"
#include "layer.h"
#include "network.h"
+#define USET
-layer make_gru_layer(int batch, int inputs, int outputs, int steps, int batch_normalize);
+layer make_rnn_layer(int batch, int inputs, int hidden, int outputs, int steps, ACTIVATION activation, int batch_normalize, int log);
-void forward_gru_layer(layer l, network_state state);
-void backward_gru_layer(layer l, network_state state);
-void update_gru_layer(layer l, int batch, float learning_rate, float momentum, float decay);
+void forward_rnn_layer(layer l, network_state state);
+void backward_rnn_layer(layer l, network_state state);
+void update_rnn_layer(layer l, int batch, float learning_rate, float momentum, float decay);
#ifdef GPU
-void forward_gru_layer_gpu(layer l, network_state state);
-void backward_gru_layer_gpu(layer l, network_state state);
-void update_gru_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay);
-void push_gru_layer(layer l);
-void pull_gru_layer(layer l);
+void forward_rnn_layer_gpu(layer l, network_state state);
+void backward_rnn_layer_gpu(layer l, network_state state);
+void update_rnn_layer_gpu(layer l, int batch, float learning_rate, float momentum, float decay);
+void push_rnn_layer(layer l);
+void pull_rnn_layer(layer l);
#endif
#endif
diff --git a/src/rnn_vid.c b/src/rnn_vid.c
index bf024f9c75661deed6f37c946b02fe1fb687772c..36912d6b65abd0d372730b4d5dcfc79bdefe1413 100644
--- a/src/rnn_vid.c
+++ b/src/rnn_vid.c
@@ -6,6 +6,8 @@
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
+image get_image_from_stream(CvCapture *cap);
+image ipl_to_image(IplImage* src);
void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters);
diff --git a/src/route_layer.c b/src/route_layer.c
index df50b64f1d8e817e062de93c14c9b7c14e02cce2..47e3d703f742cf58cb2d1ee8692e2a43a5221700 100644
--- a/src/route_layer.c
+++ b/src/route_layer.c
@@ -23,20 +23,26 @@ route_layer make_route_layer(int batch, int n, int *input_layers, int *input_siz
l.inputs = outputs;
l.delta = calloc(outputs*batch, sizeof(float));
l.output = calloc(outputs*batch, sizeof(float));;
+
+ l.forward = forward_route_layer;
+ l.backward = backward_route_layer;
#ifdef GPU
+ l.forward_gpu = forward_route_layer_gpu;
+ l.backward_gpu = backward_route_layer_gpu;
+
l.delta_gpu = cuda_make_array(l.delta, outputs*batch);
l.output_gpu = cuda_make_array(l.output, outputs*batch);
#endif
return l;
}
-void forward_route_layer(const route_layer l, network net)
+void forward_route_layer(const route_layer l, network_state state)
{
int i, j;
int offset = 0;
for(i = 0; i < l.n; ++i){
int index = l.input_layers[i];
- float *input = net.layers[index].output;
+ float *input = state.net.layers[index].output;
int input_size = l.input_sizes[i];
for(j = 0; j < l.batch; ++j){
copy_cpu(input_size, input + j*input_size, 1, l.output + offset + j*l.outputs, 1);
@@ -45,13 +51,13 @@ void forward_route_layer(const route_layer l, network net)
}
}
-void backward_route_layer(const route_layer l, network net)
+void backward_route_layer(const route_layer l, network_state state)
{
int i, j;
int offset = 0;
for(i = 0; i < l.n; ++i){
int index = l.input_layers[i];
- float *delta = net.layers[index].delta;
+ float *delta = state.net.layers[index].delta;
int input_size = l.input_sizes[i];
for(j = 0; j < l.batch; ++j){
axpy_cpu(input_size, 1, l.delta + offset + j*l.outputs, 1, delta + j*input_size, 1);
@@ -61,13 +67,13 @@ void backward_route_layer(const route_layer l, network net)
}
#ifdef GPU
-void forward_route_layer_gpu(const route_layer l, network net)
+void forward_route_layer_gpu(const route_layer l, network_state state)
{
int i, j;
int offset = 0;
for(i = 0; i < l.n; ++i){
int index = l.input_layers[i];
- float *input = net.layers[index].output_gpu;
+ float *input = state.net.layers[index].output_gpu;
int input_size = l.input_sizes[i];
for(j = 0; j < l.batch; ++j){
copy_ongpu(input_size, input + j*input_size, 1, l.output_gpu + offset + j*l.outputs, 1);
@@ -76,13 +82,13 @@ void forward_route_layer_gpu(const route_layer l, network net)
}
}
-void backward_route_layer_gpu(const route_layer l, network net)
+void backward_route_layer_gpu(const route_layer l, network_state state)
{
int i, j;
int offset = 0;
for(i = 0; i < l.n; ++i){
int index = l.input_layers[i];
- float *delta = net.layers[index].delta_gpu;
+ float *delta = state.net.layers[index].delta_gpu;
int input_size = l.input_sizes[i];
for(j = 0; j < l.batch; ++j){
axpy_ongpu(input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size, 1);
diff --git a/src/route_layer.h b/src/route_layer.h
index 1f0d6e325a53410e5f1fff276c096bb322579cac..77245a631a81a530f38a545ec23e7c57bec45acc 100644
--- a/src/route_layer.h
+++ b/src/route_layer.h
@@ -6,12 +6,12 @@
typedef layer route_layer;
route_layer make_route_layer(int batch, int n, int *input_layers, int *input_size);
-void forward_route_layer(const route_layer l, network net);
-void backward_route_layer(const route_layer l, network net);
+void forward_route_layer(const route_layer l, network_state state);
+void backward_route_layer(const route_layer l, network_state state);
#ifdef GPU
-void forward_route_layer_gpu(const route_layer l, network net);
-void backward_route_layer_gpu(const route_layer l, network net);
+void forward_route_layer_gpu(const route_layer l, network_state state);
+void backward_route_layer_gpu(const route_layer l, network_state state);
#endif
#endif
diff --git a/src/shortcut_layer.c b/src/shortcut_layer.c
index bf4551620ac79fdbb517b4b2aed3c5756162eb46..8bca50fb594081703d7644d087f3eea241cdc03a 100644
--- a/src/shortcut_layer.c
+++ b/src/shortcut_layer.c
@@ -23,7 +23,13 @@ layer make_shortcut_layer(int batch, int index, int w, int h, int c, int w2, int
l.delta = calloc(l.outputs*batch, sizeof(float));
l.output = calloc(l.outputs*batch, sizeof(float));;
+
+ l.forward = forward_shortcut_layer;
+ l.backward = backward_shortcut_layer;
#ifdef GPU
+ l.forward_gpu = forward_shortcut_layer_gpu;
+ l.backward_gpu = backward_shortcut_layer_gpu;
+
l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch);
l.output_gpu = cuda_make_array(l.output, l.outputs*batch);
#endif
diff --git a/src/softmax_layer.c b/src/softmax_layer.c
index e189701f3b6032a790f879e7b7e57b242799fd58..20bc07f32f1e04b7320a5f0e32e3cd4f3ded6619 100644
--- a/src/softmax_layer.c
+++ b/src/softmax_layer.c
@@ -19,7 +19,13 @@ softmax_layer make_softmax_layer(int batch, int inputs, int groups)
l.outputs = inputs;
l.output = calloc(inputs*batch, sizeof(float));
l.delta = calloc(inputs*batch, sizeof(float));
+
+ l.forward = forward_softmax_layer;
+ l.backward = backward_softmax_layer;
#ifdef GPU
+ l.forward_gpu = forward_softmax_layer_gpu;
+ l.backward_gpu = backward_softmax_layer_gpu;
+
l.output_gpu = cuda_make_array(l.output, inputs*batch);
l.delta_gpu = cuda_make_array(l.delta, inputs*batch);
#endif
diff --git a/src/utils.c b/src/utils.c
index 55f64b8c122881f7d420e171680c2e26ab04d70b..e8128b912f219dedf718b27ceb3e13431079088f 100644
--- a/src/utils.c
+++ b/src/utils.c
@@ -135,23 +135,20 @@ void pm(int M, int N, float *A)
printf("\n");
}
-char *find_replace(char *str, char *orig, char *rep)
+void find_replace(char *str, char *orig, char *rep, char *output)
{
- static char buffer[4096];
- static char buffer2[4096];
- static char buffer3[4096];
+ char buffer[4096] = {0};
char *p;
- if(!(p = strstr(str, orig))) // Is 'orig' even in 'str'?
- return str;
-
- strncpy(buffer2, str, p-str); // Copy characters from 'str' start to 'orig' st$
- buffer2[p-str] = '\0';
+ sprintf(buffer, "%s", str);
+ if(!(p = strstr(buffer, orig))){ // Is 'orig' even in 'str'?
+ sprintf(output, "%s", str);
+ return;
+ }
- sprintf(buffer3, "%s%s%s", buffer2, rep, p+strlen(orig));
- sprintf(buffer, "%s", buffer3);
+ *p = '\0';
- return buffer;
+ sprintf(output, "%s%s%s", buffer, rep, p+strlen(orig));
}
float sec(clock_t clocks)
diff --git a/src/utils.h b/src/utils.h
index 185e5e31d887315d6ec8851c4d16da2a7b6ddd7f..466763447025a87c310b048d8d833d688df8aa58 100644
--- a/src/utils.h
+++ b/src/utils.h
@@ -19,7 +19,7 @@ void read_all(int fd, char *buffer, size_t bytes);
void write_all(int fd, char *buffer, size_t bytes);
int read_all_fail(int fd, char *buffer, size_t bytes);
int write_all_fail(int fd, char *buffer, size_t bytes);
-char *find_replace(char *str, char *orig, char *rep);
+void find_replace(char *str, char *orig, char *rep, char *output);
void error(const char *s);
void malloc_error();
void file_error(char *s);
diff --git a/src/voxel.c b/src/voxel.c
index c277bcf291aa48008671be66aa90c394889ad396..1b53880c50df6b731e916b8887a3e819105ffe06 100644
--- a/src/voxel.c
+++ b/src/voxel.c
@@ -5,6 +5,7 @@
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
+image get_image_from_stream(CvCapture *cap);
#endif
void extract_voxel(char *lfile, char *rfile, char *prefix)
diff --git a/src/xnor_layer.c b/src/xnor_layer.c
deleted file mode 100644
index e2fca7e846f1bfded28f91432d11c43e07f5518f..0000000000000000000000000000000000000000
--- a/src/xnor_layer.c
+++ /dev/null
@@ -1,86 +0,0 @@
-#include "xnor_layer.h"
-#include "binary_convolution.h"
-#include "convolutional_layer.h"
-
-layer make_xnor_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalize)
-{
- int i;
- layer l = {0};
- l.type = XNOR;
-
- l.h = h;
- l.w = w;
- l.c = c;
- l.n = n;
- l.batch = batch;
- l.stride = stride;
- l.size = size;
- l.pad = pad;
- l.batch_normalize = batch_normalize;
-
- l.filters = calloc(c*n*size*size, sizeof(float));
- l.biases = calloc(n, sizeof(float));
-
- int out_h = convolutional_out_height(l);
- int out_w = convolutional_out_width(l);
- l.out_h = out_h;
- l.out_w = out_w;
- l.out_c = n;
- l.outputs = l.out_h * l.out_w * l.out_c;
- l.inputs = l.w * l.h * l.c;
-
- l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
-
- if(batch_normalize){
- l.scales = calloc(n, sizeof(float));
- for(i = 0; i < n; ++i){
- l.scales[i] = 1;
- }
-
- l.mean = calloc(n, sizeof(float));
- l.variance = calloc(n, sizeof(float));
-
- l.rolling_mean = calloc(n, sizeof(float));
- l.rolling_variance = calloc(n, sizeof(float));
- }
-
- l.activation = activation;
-
- fprintf(stderr, "XNOR Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
-
- return l;
-}
-
-void forward_xnor_layer(const layer l, network_state state)
-{
- int b = l.n;
- int c = l.c;
- int ix = l.w;
- int iy = l.h;
- int wx = l.size;
- int wy = l.size;
- int s = l.stride;
- int pad = l.pad * (l.size/2);
-
- // MANDATORY: Make the binary layer
- ai2_bin_conv_layer al = ai2_make_bin_conv_layer(b, c, ix, iy, wx, wy, s, pad);
-
- // OPTIONAL: You need to set the real-valued input like:
- ai2_setFltInput_unpadded(&al, state.input);
- // The above function will automatically binarize the input for the layer (channel wise).
- // If commented: using the default 0-valued input.
-
- ai2_setFltWeights(&al, l.filters);
- // The above function will automatically binarize the input for the layer (channel wise).
- // If commented: using the default 0-valued weights.
-
- // MANDATORY: Call forward
- ai2_bin_forward(&al);
-
- // OPTIONAL: Inspect outputs
- float *output = ai2_getFltOutput(&al); // output is of size l.px * l.py where px and py are the padded outputs
-
- memcpy(l.output, output, l.outputs*sizeof(float));
- // MANDATORY: Free layer
- ai2_free_bin_conv_layer(&al);
-}
diff --git a/src/xnor_layer.h b/src/xnor_layer.h
deleted file mode 100644
index f1c5b687e12c3a7507b12e9de21304afe128821a..0000000000000000000000000000000000000000
--- a/src/xnor_layer.h
+++ /dev/null
@@ -1,11 +0,0 @@
-#ifndef XNOR_LAYER_H
-#define XNOR_LAYER_H
-
-#include "layer.h"
-#include "network.h"
-
-layer make_xnor_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalization);
-void forward_xnor_layer(const layer l, network_state state);
-
-#endif
-
diff --git a/src/yolo.c b/src/yolo.c
index 2465a2cd368a332ec0f089b03f4e345060f93c8d..82faffd04ff98c87f77ddd7559c872a38897f5dc 100644
--- a/src/yolo.c
+++ b/src/yolo.c
@@ -11,7 +11,6 @@
#endif
char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
-image voc_labels[20];
void train_yolo(char *cfgfile, char *weightfile)
{
@@ -88,34 +87,6 @@ void train_yolo(char *cfgfile, char *weightfile)
save_weights(net, buff);
}
-void convert_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_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 index = i*num + n;
- int p_index = side*side*classes + i*num + n;
- float scale = predictions[p_index];
- int box_index = side*side*(classes + num) + (i*num + n)*4;
- boxes[index].x = (predictions[box_index + 0] + col) / side * w;
- boxes[index].y = (predictions[box_index + 1] + row) / side * h;
- boxes[index].w = pow(predictions[box_index + 2], (square?2:1)) * w;
- boxes[index].h = pow(predictions[box_index + 3], (square?2:1)) * h;
- for(j = 0; j < classes; ++j){
- int class_index = i*classes;
- float prob = scale*predictions[class_index+j];
- probs[index][j] = (prob > thresh) ? prob : 0;
- }
- if(only_objectness){
- probs[index][0] = scale;
- }
- }
- }
-}
-
void print_yolo_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h)
{
int i, j;
@@ -155,8 +126,6 @@ void validate_yolo(char *cfgfile, char *weightfile)
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 *));
@@ -165,9 +134,9 @@ void validate_yolo(char *cfgfile, char *weightfile)
snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
fps[j] = fopen(buff, "w");
}
- 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 *));
+ box *boxes = calloc(l.side*l.side*l.n, sizeof(box));
+ float **probs = calloc(l.side*l.side*l.n, sizeof(float *));
+ for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
int m = plist->size;
int i=0;
@@ -213,12 +182,12 @@ void validate_yolo(char *cfgfile, char *weightfile)
char *path = paths[i+t-nthreads];
char *id = basecfg(path);
float *X = val_resized[t].data;
- float *predictions = network_predict(net, X);
+ network_predict(net, X);
int w = val[t].w;
int h = val[t].h;
- convert_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes, 0);
- if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, iou_thresh);
- print_yolo_detections(fps, id, boxes, probs, side*side*l.n, classes, w, h);
+ get_detection_boxes(l, w, h, thresh, probs, boxes, 0);
+ if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, classes, iou_thresh);
+ print_yolo_detections(fps, id, boxes, probs, l.side*l.side*l.n, classes, w, h);
free(id);
free_image(val[t]);
free_image(val_resized[t]);
@@ -243,7 +212,6 @@ void validate_yolo_recall(char *cfgfile, char *weightfile)
layer l = net.layers[net.n-1];
int classes = l.classes;
- int square = l.sqrt;
int side = l.side;
int j, k;
@@ -274,14 +242,15 @@ void validate_yolo_recall(char *cfgfile, char *weightfile)
image orig = load_image_color(path, 0, 0);
image sized = resize_image(orig, net.w, net.h);
char *id = basecfg(path);
- float *predictions = network_predict(net, sized.data);
- convert_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 1);
+ network_predict(net, sized.data);
+ get_detection_boxes(l, orig.w, orig.h, thresh, probs, boxes, 1);
if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms);
- char *labelpath = find_replace(path, "images", "labels");
- labelpath = find_replace(labelpath, "JPEGImages", "labels");
- labelpath = find_replace(labelpath, ".jpg", ".txt");
- labelpath = find_replace(labelpath, ".JPEG", ".txt");
+ char labelpath[4096];
+ find_replace(path, "images", "labels", labelpath);
+ find_replace(labelpath, "JPEGImages", "labels", labelpath);
+ find_replace(labelpath, ".jpg", ".txt", labelpath);
+ find_replace(labelpath, ".JPEG", ".txt", labelpath);
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
@@ -315,7 +284,7 @@ void validate_yolo_recall(char *cfgfile, char *weightfile)
void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
{
-
+ image *alphabet = load_alphabet();
network net = parse_network_cfg(cfgfile);
if(weightfile){
load_weights(&net, weightfile);
@@ -345,12 +314,12 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
image sized = resize_image(im, net.w, net.h);
float *X = sized.data;
time=clock();
- float *predictions = network_predict(net, X);
+ network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
- convert_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0);
+ get_detection_boxes(l, 1, 1, thresh, probs, boxes, 1);
if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
- //draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, voc_labels, 20);
- draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, voc_labels, 20);
+ //draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20);
+ draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20);
save_image(im, "predictions");
show_image(im, "predictions");
@@ -366,13 +335,7 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
void run_yolo(int argc, char **argv)
{
- int i;
- for(i = 0; i < 20; ++i){
- char buff[256];
- sprintf(buff, "data/labels/%s.png", voc_names[i]);
- voc_labels[i] = load_image_color(buff, 0, 0);
- }
-
+ char *prefix = find_char_arg(argc, argv, "-prefix", 0);
float thresh = find_float_arg(argc, argv, "-thresh", .2);
int cam_index = find_int_arg(argc, argv, "-c", 0);
int frame_skip = find_int_arg(argc, argv, "-s", 0);
@@ -388,5 +351,5 @@ void run_yolo(int argc, char **argv)
else if(0==strcmp(argv[2], "train")) train_yolo(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights);
else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights);
- else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, voc_names, voc_labels, 20, frame_skip);
+ else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, voc_names, 20, frame_skip, prefix);
}