going to break stuff

src/convolutional_kernels.cu

@@ -17,7 +17,7 @@ __global__ void bias_output_kernel(float *output, float *biases, int n, int size
     if(offset < size) output[(batch*n+filter)*size + offset] = biases[filter];
 }
 
-extern "C" void bias_output_gpu(float *output, float *biases, int batch, int n, int size)
+void bias_output_gpu(float *output, float *biases, int batch, int n, int size)
 {
     dim3 dimBlock(BLOCK, 1, 1);
     dim3 dimGrid((size-1)/BLOCK + 1, n, batch);
@@ -46,13 +46,13 @@ __global__ void backward_bias_kernel(float *bias_updates, float *delta, int batc
     }
 }
 
-extern "C" void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size)
+void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size)
 {
     backward_bias_kernel<<<n, BLOCK>>>(bias_updates, delta, batch, n, size, 1);
     check_error(cudaPeekAtLastError());
 }
 
-extern "C" void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
+void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
 {
     int i;
     int m = layer.n;
@@ -71,7 +71,7 @@ extern "C" void forward_convolutional_layer_gpu(convolutional_layer layer, netwo
     activate_array_ongpu(layer.output_gpu, m*n*layer.batch, layer.activation);
 }
 
-extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
+void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
 {
     int i;
     int m = layer.n;
@@ -105,7 +105,7 @@ extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, netw
     }
 }
 
-extern "C" void pull_convolutional_layer(convolutional_layer layer)
+void pull_convolutional_layer(convolutional_layer layer)
 {
     cuda_pull_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size);
     cuda_pull_array(layer.biases_gpu, layer.biases, layer.n);
@@ -113,7 +113,7 @@ extern "C" void pull_convolutional_layer(convolutional_layer layer)
     cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
 }
 
-extern "C" void push_convolutional_layer(convolutional_layer layer)
+void push_convolutional_layer(convolutional_layer layer)
 {
     cuda_push_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size);
     cuda_push_array(layer.biases_gpu, layer.biases, layer.n);
@@ -121,7 +121,7 @@ extern "C" void push_convolutional_layer(convolutional_layer layer)
     cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
 }
 
-extern "C" void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay)
+void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay)
 {
     int size = layer.size*layer.size*layer.c*layer.n;
 

src/detection.c

@@ -3,11 +3,11 @@
 #include "parser.h"
 
 
-char *class_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
+char *class_names[] = {"bg", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
 #define AMNT 3
 void draw_detection(image im, float *box, int side)
 {
-    int classes = 20;
+    int classes = 21;
     int elems = 4+classes;
     int j;
     int r, c;
@@ -50,6 +50,7 @@ void train_detection(char *cfgfile, char *weightfile)
     if(weightfile){
         load_weights(&net, weightfile);
     }
+    net.seen = 0;
     printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     int imgs = 128;
     srand(time(0));
@@ -62,7 +63,7 @@ void train_detection(char *cfgfile, char *weightfile)
     int im_dim = 512;
     int jitter = 64;
     int classes = 20;
-    int background = 1;
+    int background = 0;
     pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, background, &buffer);
     clock_t time;
     while(1){
@@ -72,12 +73,12 @@ void train_detection(char *cfgfile, char *weightfile)
         train = buffer;
         load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, background, &buffer);
 
-        /*
-           image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[0]);
-           draw_detection(im, train.y.vals[0], 7);
+/*
+           image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[114]);
+           draw_detection(im, train.y.vals[114], 7);
            show_image(im, "truth");
            cvWaitKey(0);
-         */
+*/
 
         printf("Loaded: %lf seconds\n", sec(clock()-time));
         time=clock();
@@ -108,7 +109,7 @@ void validate_detection(char *cfgfile, char *weightfile)
     char **paths = (char **)list_to_array(plist);
     int im_size = 448;
     int classes = 20;
-    int background = 1;
+    int background = 0;
     int num_output = 7*7*(4+classes+background);
 
     int m = plist->size;

src/dropout_layer_kernels.cu

@@ -11,7 +11,7 @@ __global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand
     if(id < size) input[id] = (rand[id] < prob) ? 0 : input[id]*scale;
 }
 
-extern "C" void forward_dropout_layer_gpu(dropout_layer layer, network_state state)
+void forward_dropout_layer_gpu(dropout_layer layer, network_state state)
 {
     if (!state.train) return;
     int size = layer.inputs*layer.batch;
@@ -21,7 +21,7 @@ extern "C" void forward_dropout_layer_gpu(dropout_layer layer, network_state sta
     check_error(cudaPeekAtLastError());
 }
 
-extern "C" void backward_dropout_layer_gpu(dropout_layer layer, network_state state)
+void backward_dropout_layer_gpu(dropout_layer layer, network_state state)
 {
     if(!state.delta) return;
     int size = layer.inputs*layer.batch;

src/network.c

@@ -194,24 +194,6 @@ float *get_network_delta(network net)
     return get_network_delta_layer(net, net.n-1);
 }
 
-float calculate_error_network(network net, float *truth)
-{
-    float sum = 0;
-    float *delta = get_network_delta(net);
-    float *out = get_network_output(net);
-    int i;
-    for(i = 0; i < get_network_output_size(net)*net.batch; ++i){
-        //if(i %get_network_output_size(net) == 0) printf("\n");
-        //printf("%5.2f %5.2f, ", out[i], truth[i]);
-        //if(i == get_network_output_size(net)) printf("\n");
-        delta[i] = truth[i] - out[i];
-        //printf("%.10f, ", out[i]);
-        sum += delta[i]*delta[i];
-    }
-    //printf("\n");
-    return sum;
-}
-
 int get_predicted_class_network(network net)
 {
     float *out = get_network_output(net);

src/network_kernels.cu

@@ -20,8 +20,8 @@ extern "C" {
 #include "dropout_layer.h"
 }
 
-extern "C" float * get_network_output_gpu_layer(network net, int i);
-extern "C" float * get_network_delta_gpu_layer(network net, int i);
+float * get_network_output_gpu_layer(network net, int i);
+float * get_network_delta_gpu_layer(network net, int i);
 float *get_network_output_gpu(network net);
 
 void forward_network_gpu(network net, network_state state)
@@ -196,8 +196,8 @@ float train_network_datum_gpu(network net, float *x, float *y)
     state.train = 1;
     forward_network_gpu(net, state);
     backward_network_gpu(net, state);
-    if ((net.seen / net.batch) % net.subdivisions == 0) update_network_gpu(net);
     float error = get_network_cost(net);
+    if ((net.seen / net.batch) % net.subdivisions == 0) update_network_gpu(net);
 
     return error;
 }