stable

src/axpy.cl

@@ -10,6 +10,12 @@ __kernel void scal(int N, float ALPHA, __global float *X, int INCX)
     X[i*INCX] *= ALPHA;
 }
 
+__kernel void mask(int n, __global float *x, __global float *mask, int mod)
+{
+    int i = get_global_id(0);
+    x[i] = (mask[(i/mod)*mod]) ? x[i] : 0;
+}
+
 __kernel void copy(int N, __global float *X, int OFFX, int INCX, __global float *Y, int OFFY, int INCY)
 {
     int i = get_global_id(0);

src/cnn.c

@@ -314,15 +314,14 @@ void train_detection_net()
     int imgs = 1000/net.batch+1;
     srand(time(0));
     int i = 0;
-    char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
+    list *plist = get_paths("/home/pjreddie/data/imagenet/horse.txt");
-    list *plist = get_paths("/data/imagenet/cls.train.list");
     char **paths = (char **)list_to_array(plist);
     printf("%d\n", plist->size);
     clock_t time;
     while(1){
         i += 1;
         time=clock();
-        data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
+        data train = load_data_detection_random(imgs*net.batch, paths, plist->size, 256, 256, 8, 8, 256);
         //translate_data_rows(train, -144);
         normalize_data_rows(train);
         printf("Loaded: %lf seconds\n", sec(clock()-time));
@@ -346,7 +345,7 @@ void train_imagenet()
 {
     float avg_loss = 1;
     //network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
-    network net = parse_network_cfg("cfg/alexnet.cfg");
+    network net = parse_network_cfg("cfg/trained_alexnet.cfg");
     printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     int imgs = 1000/net.batch+1;
     srand(time(0));
@@ -412,6 +411,29 @@ void validate_imagenet(char *filename)
     }
 }
 
+void test_detection()
+{
+    network net = parse_network_cfg("cfg/detnet_test.cfg");
+    //imgs=1;
+    srand(2222222);
+    int i = 0;
+    clock_t time;
+    char filename[256];
+    int indexes[10];
+    while(1){
+        fgets(filename, 256, stdin);
+        image im = load_image_color(filename, 256, 256);
+        z_normalize_image(im);
+        printf("%d %d %d\n", im.h, im.w, im.c);
+        float *X = im.data;
+        time=clock();
+        float *predictions = network_predict(net, X);
+        top_predictions(net, 10, indexes);
+        printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
+        free_image(im);
+    }
+}
+
 void test_imagenet()
 {
     network net = parse_network_cfg("cfg/imagenet_test.cfg");
@@ -717,6 +739,7 @@ int main(int argc, char *argv[])
         return 0;
     }
     if(0==strcmp(argv[1], "train")) train_imagenet();
+    else if(0==strcmp(argv[1], "detection")) train_detection_net();
     else if(0==strcmp(argv[1], "asirra")) train_asirra();
     else if(0==strcmp(argv[1], "nist")) train_nist();
     else if(0==strcmp(argv[1], "test_correct")) test_gpu_net();
@@ -726,7 +749,6 @@ int main(int argc, char *argv[])
 #ifdef GPU
     else if(0==strcmp(argv[1], "test_gpu")) test_gpu_blas();
 #endif
-    test_parser();
     fprintf(stderr, "Success!\n");
     return 0;
 }

src/cost_layer.c

@@ -2,15 +2,36 @@
 #include "utils.h"
 #include "mini_blas.h"
 #include <math.h>
+#include <string.h>
 #include <stdlib.h>
 #include <stdio.h>
 
-cost_layer *make_cost_layer(int batch, int inputs)
+COST_TYPE get_cost_type(char *s)
+{
+    if (strcmp(s, "sse")==0) return SSE;
+    if (strcmp(s, "detection")==0) return DETECTION;
+    fprintf(stderr, "Couldn't find activation function %s, going with SSE\n", s);
+    return SSE;
+}
+
+char *get_cost_string(COST_TYPE a)
+{
+    switch(a){
+        case SSE:
+            return "sse";
+        case DETECTION:
+            return "detection";
+    }
+    return "sse";
+}
+
+cost_layer *make_cost_layer(int batch, int inputs, COST_TYPE type)
 {
     fprintf(stderr, "Cost Layer: %d inputs\n", inputs);
     cost_layer *layer = calloc(1, sizeof(cost_layer));
     layer->batch = batch;
     layer->inputs = inputs;
+    layer->type = type;
     layer->delta = calloc(inputs*batch, sizeof(float));
     layer->output = calloc(1, sizeof(float));
     #ifdef GPU
@@ -24,6 +45,12 @@ void forward_cost_layer(cost_layer layer, float *input, float *truth)
     if (!truth) return;
     copy_cpu(layer.batch*layer.inputs, truth, 1, layer.delta, 1);
     axpy_cpu(layer.batch*layer.inputs, -1, input, 1, layer.delta, 1);
+    if(layer.type == DETECTION){
+        int i;
+        for(i = 0; i < layer.batch*layer.inputs; ++i){
+            if((i%5) && !truth[(i/5)*5]) layer.delta[i] = 0;
+        }
+    }
     *(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1);
 }
 
@@ -33,6 +60,38 @@ void backward_cost_layer(const cost_layer layer, float *input, float *delta)
 }
 
 #ifdef GPU
+
+cl_kernel get_mask_kernel()
+{
+    static int init = 0;
+    static cl_kernel kernel;
+    if(!init){
+        kernel = get_kernel("src/axpy.cl", "mask", 0);
+        init = 1;
+    }
+    return kernel;
+}
+
+void mask_ongpu(int n, cl_mem x, cl_mem mask, int mod)
+{
+    cl_setup();
+    cl_kernel kernel = get_mask_kernel();
+    cl_command_queue queue = cl.queue;
+
+    cl_uint i = 0;
+    cl.error = clSetKernelArg(kernel, i++, sizeof(n), (void*) &n);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(x), (void*) &x);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(mask), (void*) &mask);
+    cl.error = clSetKernelArg(kernel, i++, sizeof(mod), (void*) &mod);
+    check_error(cl);
+
+    const size_t global_size[] = {n};
+
+    cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
+    check_error(cl);
+
+}
+
 void forward_cost_layer_gpu(cost_layer layer, cl_mem input, cl_mem truth)
 {
     if (!truth) return;
@@ -40,6 +99,10 @@ void forward_cost_layer_gpu(cost_layer layer, cl_mem input, cl_mem truth)
     copy_ongpu(layer.batch*layer.inputs, truth, 1, layer.delta_cl, 1);
     axpy_ongpu(layer.batch*layer.inputs, -1, input, 1, layer.delta_cl, 1);
 
+    if(layer.type==DETECTION){
+        mask_ongpu(layer.inputs*layer.batch, layer.delta_cl, truth, 5);
+    }
+
     cl_read_array(layer.delta_cl, layer.delta, layer.batch*layer.inputs);
     *(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1);
     //printf("%f\n", *layer.output);

src/cost_layer.h

@@ -2,17 +2,24 @@
 #define COST_LAYER_H
 #include "opencl.h"
 
+typedef enum{
+    SSE, DETECTION
+} COST_TYPE;
+
 typedef struct {
     int inputs;
     int batch;
     float *delta;
     float *output;
+    COST_TYPE type;
     #ifdef GPU
     cl_mem delta_cl;
     #endif
 } cost_layer;
 
-cost_layer *make_cost_layer(int batch, int inputs);
+COST_TYPE get_cost_type(char *s);
+char *get_cost_string(COST_TYPE a);
+cost_layer *make_cost_layer(int batch, int inputs, COST_TYPE type);
 void forward_cost_layer(const cost_layer layer, float *input, float *truth);
 void backward_cost_layer(const cost_layer layer, float *input, float *delta);
 

src/data.c

@@ -26,6 +26,7 @@ void fill_truth_detection(char *path, float *truth, int height, int width, int n
     char *labelpath = find_replace(path, "imgs", "det");
     labelpath = find_replace(labelpath, ".JPEG", ".txt");
     FILE *file = fopen(labelpath, "r");
+    if(!file) file_error(labelpath);
     int x, y, h, w;
     while(fscanf(file, "%d %d %d %d", &x, &y, &w, &h) == 4){
         int i = x/box_width;
@@ -34,6 +35,7 @@ void fill_truth_detection(char *path, float *truth, int height, int width, int n
         float dw = (float)(y%box_width)/box_width;
         float sh = h/scale;
         float sw = w/scale;
+        //printf("%d %d %f %f\n", i, j, dh, dw);
         int index = (i+j*num_width)*5;
         truth[index++] = 1;
         truth[index++] = dh;
@@ -41,6 +43,7 @@ void fill_truth_detection(char *path, float *truth, int height, int width, int n
         truth[index++] = sh;
         truth[index++] = sw;
     }
+    fclose(file);
 }
 
 void fill_truth(char *path, char **labels, int k, float *truth)
@@ -125,7 +128,7 @@ void free_data(data d)
     }
 }
 
-data load_data_detection_random(int n, char **paths, int m, char **labels, int h, int w, int nh, int nw, float scale)
+data load_data_detection_random(int n, char **paths, int m, int h, int w, int nh, int nw, float scale)
 {
     char **random_paths = calloc(n, sizeof(char*));
     int i;

src/data.h

@@ -14,7 +14,7 @@ typedef struct{
 void free_data(data d);
 data load_data(char **paths, int n, char **labels, int k, int h, int w);
 data load_data_random(int n, char **paths, int m, char **labels, int k, int h, int w);
-data load_data_detection_random(int n, char **paths, int m, char **labels, int h, int w, int nh, int nw, float scale);
+data load_data_detection_random(int n, char **paths, int m, int h, int w, int nh, int nw, float scale);
 data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w);
 data load_cifar10_data(char *filename);
 data load_all_cifar10();

src/parser.c

@@ -165,7 +165,9 @@ cost_layer *parse_cost(list *options, network *net, int count)
     }else{
         input =  get_network_output_size_layer(*net, count-1);
     }
-    cost_layer *layer = make_cost_layer(net->batch, input);
+    char *type_s = option_find_str(options, "type", "sse");
+    COST_TYPE type = get_cost_type(type_s);
+    cost_layer *layer = make_cost_layer(net->batch, input, type);
     option_unused(options);
     return layer;
 }
@@ -565,7 +567,7 @@ void print_softmax_cfg(FILE *fp, softmax_layer *l, network net, int count)
 
 void print_cost_cfg(FILE *fp, cost_layer *l, network net, int count)
 {
-    fprintf(fp, "[cost]\n");
+    fprintf(fp, "[cost]\ntype=%s\n", get_cost_string(l->type));
     if(count == 0) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
     fprintf(fp, "\n");
 }