Added Darknet reference model

Joseph Redmon

2015-08-17 741ada451cc7fee1b9a4c3deaec6af87a2af7497

Added Darknet reference model

 cfg/darknet.cfg

New file
@@ -0,0 +1,154 @@
[net]
batch=128
subdivisions=1
height=256
width=256
channels=3
learning_rate=0.01
momentum=0.9
decay=0.0005

[crop]
crop_height=224
crop_width=224
flip=1
angle=0
saturation=1
exposure=1

[convolutional]
filters=16
size=3
stride=1
pad=1
activation=leaky

#[convolutional]
#filters=16
#size=3
#stride=1
#pad=1
#activation=leaky

[maxpool]
stride=2
size=2

[convolutional]
filters=32
size=3
stride=1
pad=1
activation=leaky

#[convolutional]
#filters=32
#size=3
#stride=1
#pad=1
#activation=leaky

[maxpool]
stride=2
size=2

[convolutional]
filters=64
size=3
stride=1
pad=1
activation=leaky

#[convolutional]
#filters=64
#size=3
#stride=1
#pad=1
#activation=leaky

[maxpool]
stride=2
size=2

[convolutional]
filters=128
size=3
stride=1
pad=1
activation=leaky

#[convolutional]
#filters=128
#size=3
#stride=1
#pad=1
#activation=leaky

[maxpool]
stride=2
size=2

[convolutional]
filters=256
size=3
stride=1
pad=1
activation=leaky

#[convolutional]
#filters=256
#size=3
#stride=1
#pad=1
#activation=leaky

[maxpool]
stride=2
size=2

[convolutional]
filters=512
size=3
stride=1
pad=1
activation=leaky

#[convolutional]
#filters=512
#size=3
#stride=1
#pad=1
#activation=leaky

[maxpool]
stride=2
size=2

[convolutional]
filters=1024
size=3
stride=1
pad=1
activation=leaky

#[convolutional]
#filters=1024
#size=3
#stride=1
#pad=1
#activation=leaky

[avgpool]

[dropout]
probability=.5

[connected]
output=1000
activation=leaky

[softmax]

[cost]
type=sse


 src/darknet.c

@@ -5,6 +5,7 @@
#include "parser.h"
#include "utils.h"
#include "cuda.h"
#include "blas.h"

#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
@@ -33,8 +34,55 @@
    fclose(fp);
}

void average(int argc, char *argv[])
{
    char *cfgfile = argv[2];
    char *outfile = argv[3];
    gpu_index = -1;
    network net = parse_network_cfg(cfgfile);
    network sum = parse_network_cfg(cfgfile);

    char *weightfile = argv[4];   
    load_weights(&sum, weightfile);

    int i, j;
    int n = argc - 5;
    for(i = 0; i < n; ++i){
        weightfile = argv[i+5];   
        load_weights(&net, weightfile);
        for(j = 0; j < net.n; ++j){
            layer l = net.layers[j];
            layer out = sum.layers[j];
            if(l.type == CONVOLUTIONAL){
                int num = l.n*l.c*l.size*l.size;
                axpy_cpu(l.n, 1, l.biases, 1, out.biases, 1);
                axpy_cpu(num, 1, l.filters, 1, out.filters, 1);
            }
            if(l.type == CONNECTED){
                axpy_cpu(l.outputs, 1, l.biases, 1, out.biases, 1);
                axpy_cpu(l.outputs*l.inputs, 1, l.weights, 1, out.weights, 1);
            }
        }
    }
    n = n+1;
    for(j = 0; j < net.n; ++j){
        layer l = sum.layers[j];
        if(l.type == CONVOLUTIONAL){
            int num = l.n*l.c*l.size*l.size;
            scal_cpu(l.n, 1./n, l.biases, 1);
            scal_cpu(num, 1./n, l.filters, 1);
        }
        if(l.type == CONNECTED){
            scal_cpu(l.outputs, 1./n, l.biases, 1);
            scal_cpu(l.outputs*l.inputs, 1./n, l.weights, 1);
        }
    }
    save_weights(sum, outfile);
}

void partial(char *cfgfile, char *weightfile, char *outfile, int max)
{
    gpu_index = -1;
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights_upto(&net, weightfile, max);
@@ -114,6 +162,8 @@

    if(0==strcmp(argv[1], "imagenet")){
        run_imagenet(argc, argv);
    } else if (0 == strcmp(argv[1], "average")){
        average(argc, argv);
    } else if (0 == strcmp(argv[1], "detection")){
        run_detection(argc, argv);
    } else if (0 == strcmp(argv[1], "yolo")){