IOU loss function

Joseph Redmon

2015-04-24 989ab8c38a02fa7ea9c25108151736c62e81c972

 src/normalization_layer.c

@@ -6,7 +6,7 @@
    int h = layer.h;
    int w = layer.w;
    int c = layer.c;
    return float_to_image(h,w,c,layer.output);
    return float_to_image(w,h,c,layer.output);
}

image get_normalization_delta(normalization_layer layer)
@@ -14,7 +14,7 @@
    int h = layer.h;
    int w = layer.w;
    int c = layer.c;
    return float_to_image(h,w,c,layer.delta);
    return float_to_image(w,h,c,layer.delta);
}

normalization_layer *make_normalization_layer(int batch, int h, int w, int c, int size, float alpha, float beta, float kappa)
@@ -35,13 +35,12 @@
    return layer;
}

void resize_normalization_layer(normalization_layer *layer, int h, int w, int c)
void resize_normalization_layer(normalization_layer *layer, int h, int w)
{
    layer->h = h;
    layer->w = w;
    layer->c = c;
    layer->output = realloc(layer->output, h * w * c * layer->batch * sizeof(float));
    layer->delta = realloc(layer->delta, h * w * c * layer->batch * sizeof(float));
    layer->output = realloc(layer->output, h * w * layer->c * layer->batch * sizeof(float));
    layer->delta = realloc(layer->delta, h * w * layer->c * layer->batch * sizeof(float));
    layer->sums = realloc(layer->sums, h*w * sizeof(float));
}

@@ -60,28 +59,29 @@
    }
}

void forward_normalization_layer(const normalization_layer layer, float *in)
void forward_normalization_layer(const normalization_layer layer, network_state state)
{
    int i,j,k;
    memset(layer.sums, 0, layer.h*layer.w*sizeof(float));
    int imsize = layer.h*layer.w;
    for(j = 0; j < layer.size/2; ++j){
        if(j < layer.c) add_square_array(in+j*imsize, layer.sums, imsize);
        if(j < layer.c) add_square_array(state.input+j*imsize, layer.sums, imsize);
    }
    for(k = 0; k < layer.c; ++k){
        int next = k+layer.size/2;
        int prev = k-layer.size/2-1;
        if(next < layer.c) add_square_array(in+next*imsize, layer.sums, imsize);
        if(prev > 0)        sub_square_array(in+prev*imsize, layer.sums, imsize);
        if(next < layer.c) add_square_array(state.input+next*imsize, layer.sums, imsize);
        if(prev > 0)       sub_square_array(state.input+prev*imsize, layer.sums, imsize);
        for(i = 0; i < imsize; ++i){
            layer.output[k*imsize + i] = in[k*imsize+i] / pow(layer.kappa + layer.alpha * layer.sums[i], layer.beta);
            layer.output[k*imsize + i] = state.input[k*imsize+i] / pow(layer.kappa + layer.alpha * layer.sums[i], layer.beta);
        }
    }
}

void backward_normalization_layer(const normalization_layer layer, float *in, float *delta)
void backward_normalization_layer(const normalization_layer layer, network_state state)
{
    //TODO!
    // TODO!
    // OR NOT TODO!!
}

void visualize_normalization_layer(normalization_layer layer, char *window)