#include "detection_layer.h"
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#include "activations.h"
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#include "softmax_layer.h"
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#include "blas.h"
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#include "cuda.h"
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#include <stdio.h>
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#include <stdlib.h>
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int get_detection_layer_locations(detection_layer layer)
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{
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return layer.inputs / (layer.classes+layer.coords+layer.rescore+layer.background);
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}
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int get_detection_layer_output_size(detection_layer layer)
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{
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return get_detection_layer_locations(layer)*(layer.background + layer.classes + layer.coords);
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}
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detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background, int nuisance)
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{
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detection_layer *layer = calloc(1, sizeof(detection_layer));
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layer->batch = batch;
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layer->inputs = inputs;
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layer->classes = classes;
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layer->coords = coords;
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layer->rescore = rescore;
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layer->nuisance = nuisance;
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layer->background = background;
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int outputs = get_detection_layer_output_size(*layer);
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layer->output = calloc(batch*outputs, sizeof(float));
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layer->delta = calloc(batch*outputs, sizeof(float));
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#ifdef GPU
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layer->output_gpu = cuda_make_array(0, batch*outputs);
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layer->delta_gpu = cuda_make_array(0, batch*outputs);
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#endif
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fprintf(stderr, "Detection Layer\n");
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srand(0);
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return layer;
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}
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void dark_zone(detection_layer layer, int class, int start, network_state state)
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{
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int index = start+layer.background+class;
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int size = layer.classes+layer.coords+layer.background;
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int location = (index%(7*7*size)) / size ;
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int r = location / 7;
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int c = location % 7;
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int dr, dc;
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for(dr = -1; dr <= 1; ++dr){
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for(dc = -1; dc <= 1; ++dc){
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if(!(dr || dc)) continue;
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if((r + dr) > 6 || (r + dr) < 0) continue;
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if((c + dc) > 6 || (c + dc) < 0) continue;
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int di = (dr*7 + dc) * size;
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if(state.truth[index+di]) continue;
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layer.output[index + di] = 0;
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//if(!state.truth[start+di]) continue;
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//layer.output[start + di] = 1;
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}
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}
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}
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void forward_detection_layer(const detection_layer layer, network_state state)
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{
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int in_i = 0;
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int out_i = 0;
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int locations = get_detection_layer_locations(layer);
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int i,j;
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for(i = 0; i < layer.batch*locations; ++i){
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int mask = (!state.truth || state.truth[out_i + layer.background + layer.classes + 2]);
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float scale = 1;
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if(layer.rescore) scale = state.input[in_i++];
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else if(layer.nuisance){
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layer.output[out_i++] = 1-state.input[in_i++];
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scale = mask;
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}
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else if(layer.background) layer.output[out_i++] = scale*state.input[in_i++];
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for(j = 0; j < layer.classes; ++j){
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layer.output[out_i++] = scale*state.input[in_i++];
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}
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if(layer.nuisance){
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}else if(layer.background){
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softmax_array(layer.output + out_i - layer.classes-layer.background, layer.classes+layer.background, layer.output + out_i - layer.classes-layer.background);
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activate_array(state.input+in_i, layer.coords, LOGISTIC);
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}
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for(j = 0; j < layer.coords; ++j){
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layer.output[out_i++] = mask*state.input[in_i++];
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}
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}
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/*
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if(layer.background || 1){
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for(i = 0; i < layer.batch*locations; ++i){
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int index = i*(layer.classes+layer.coords+layer.background);
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for(j= 0; j < layer.classes; ++j){
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if(state.truth[index+j+layer.background]){
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//dark_zone(layer, j, index, state);
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}
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}
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}
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}
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*/
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}
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void backward_detection_layer(const detection_layer layer, network_state state)
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{
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int locations = get_detection_layer_locations(layer);
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int i,j;
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int in_i = 0;
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int out_i = 0;
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for(i = 0; i < layer.batch*locations; ++i){
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float scale = 1;
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float latent_delta = 0;
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if(layer.rescore) scale = state.input[in_i++];
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else if (layer.nuisance) state.delta[in_i++] = -layer.delta[out_i++];
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else if (layer.background) state.delta[in_i++] = scale*layer.delta[out_i++];
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for(j = 0; j < layer.classes; ++j){
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latent_delta += state.input[in_i]*layer.delta[out_i];
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state.delta[in_i++] = scale*layer.delta[out_i++];
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}
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if (layer.nuisance) ;
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else if (layer.background) gradient_array(layer.output + out_i, layer.coords, LOGISTIC, layer.delta + out_i);
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for(j = 0; j < layer.coords; ++j){
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state.delta[in_i++] = layer.delta[out_i++];
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}
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if(layer.rescore) state.delta[in_i-layer.coords-layer.classes-layer.rescore-layer.background] = latent_delta;
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}
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}
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#ifdef GPU
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void forward_detection_layer_gpu(const detection_layer layer, network_state state)
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{
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int outputs = get_detection_layer_output_size(layer);
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float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
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float *truth_cpu = 0;
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if(state.truth){
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truth_cpu = calloc(layer.batch*outputs, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, layer.batch*outputs);
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}
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cuda_pull_array(state.input, in_cpu, layer.batch*layer.inputs);
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network_state cpu_state;
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cpu_state.train = state.train;
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cpu_state.truth = truth_cpu;
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cpu_state.input = in_cpu;
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forward_detection_layer(layer, cpu_state);
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cuda_push_array(layer.output_gpu, layer.output, layer.batch*outputs);
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free(cpu_state.input);
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if(cpu_state.truth) free(cpu_state.truth);
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}
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void backward_detection_layer_gpu(detection_layer layer, network_state state)
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{
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int outputs = get_detection_layer_output_size(layer);
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float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
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float *delta_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
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float *truth_cpu = 0;
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if(state.truth){
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truth_cpu = calloc(layer.batch*outputs, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, layer.batch*outputs);
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}
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network_state cpu_state;
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cpu_state.train = state.train;
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cpu_state.input = in_cpu;
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cpu_state.truth = truth_cpu;
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cpu_state.delta = delta_cpu;
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cuda_pull_array(state.input, in_cpu, layer.batch*layer.inputs);
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cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*outputs);
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backward_detection_layer(layer, cpu_state);
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cuda_push_array(state.delta, delta_cpu, layer.batch*layer.inputs);
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free(in_cpu);
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free(delta_cpu);
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}
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#endif
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