Darknet - Custom Layer

Note: This post is based on AlexeyAB/darknet version, the procedure of pjreddie/darknet version may differ slightly (have not tried, maybe identical).

8 steps to build your own deep learning lego module in Darknet:

Define LAYER_TYPE
Add LAYER_TYPE for your custom layer in layer.h
```
typedef enum {
    // ...
    CUSTOM;
} LAYER_TYPE;
```
Define layer string
Add layer string for your custom layer in parser.c
```
LAYER_TYPE string_to_layer_type(char * type)
{
// ...
if (strcmp(type, "[custom]")==0) return CUSTOM;
}
```
Then darknet would be able to recognize your custom layer in cfg file:
```
[net]
#...
[custom]
#...
```

Implement your custom layer: custom_layer.c and custom_layer.h
Should contain at least 4 functions:

layer make_custom_layer(int batch, int w, int h, .....);
void forward_custom_layer(const layer l, network_state state);
void backward_custom_layer(const layer l, network_state state);
void resize_custom_layer(layer *l, int w, int h);

(optional) If you want to train it with GPU, implement these:

#ifdef GPU
void forward_custom_layer_gpu(const layer l, network_state state);
void backward_custom_layer_gpu(const layer l, network_state state);
#endif

In parser.c

Include source file of your custom layer(to use make_custom_layer()):

#include "custom_layer.h"

Implement the parse function:

layer parse_custom(list *options, size_params params)
{	
    int param1 = option_find_int(options, "param1", 1);
    //...
    layer l = make_custom_layer(params.batch, params.w, params.h, param1, ...);
    l.param2 = option_find_float(options, "param2", .1);
    //...
    return l;
}

Add your parse function in parse_network_cfg_custom():

network parse_network_cfg_custom(char *filename, int batch)
{
    //...
    while(n){
        //...
        LAYER_TYPE lt = string_to_layer_type(s->type);
        if(lt == CONVOLUTIONAL){
            l = parse_convolutional(options, params);
        }else if(lt == CUSTOM){
            l = parse_custom(options, params);
        }
    }
    //...
    return net;
}

In network.c

Include source file of your custom layer(to use resize_custom_layer()):

#include "custom_layer.h"

Modify int resize_network(network *net, int w, int h) function:

int resize_network(network *net, int w, int h)
{
    //...
    for (i = 0; i < net->n; ++i){
        layer l = net->layers[i];
        if(l.type == CONVOLUTIONAL){
            resize_convolutional_layer(&l, w, h);
        }else if(l.type == CUSTOM){
            resize_custom_layer(&l, w, h);
        }

[optional] If your custom layer is used to produce results(like YOLO, REGION or DETECTION):

Implement custom_num_detections() and get_custom_detections() in custom_layer.c, then modify 2 functions in network.c (to count the detections and get the detections):

int num_detections(network *net, float thresh)
{
    int i;
    int s = 0;
    for (i = 0; i < net->n; ++i) {
        if (l.type == CUSTOM) {
             s += custom_num_detections(l, thresh);
        }
    //...
    }
    return s;
}

void fill_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets, int letter)
{
     int prev_classes = -1;
     int j;
     for (j = 0; j < net->n; ++j) {
         layer l = net->layers[j];
         if (l.type == CUSTOM){
             int count = get_custom_detections(...);
             //...
         }
         //...
     }
}

Add custom_layer.c and custom_layer.h in your Visual Studio Solution build/darknet.sln

Or add custom_layer.o in your Makefile
Rebuild your project

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