#include "region_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 "box.h"
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#include "cuda.h"
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#include "utils.h"
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#include <stdio.h>
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#include <assert.h>
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#include <string.h>
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#include <stdlib.h>
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region_layer make_region_layer(int batch, int inputs, int n, int side, int classes, int coords, int rescore)
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{
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region_layer l = {0};
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l.type = REGION;
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l.n = n;
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l.batch = batch;
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l.inputs = inputs;
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l.classes = classes;
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l.coords = coords;
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l.rescore = rescore;
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l.side = side;
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assert(side*side*l.coords*l.n == inputs);
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l.cost = calloc(1, sizeof(float));
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int outputs = l.n*5*side*side;
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l.outputs = outputs;
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l.output = calloc(batch*outputs, sizeof(float));
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l.delta = calloc(batch*inputs, sizeof(float));
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#ifdef GPU
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l.output_gpu = cuda_make_array(l.output, batch*outputs);
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l.delta_gpu = cuda_make_array(l.delta, batch*inputs);
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#endif
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fprintf(stderr, "Region Layer\n");
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srand(0);
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return l;
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}
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void forward_region_layer(const region_layer l, network_state state)
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{
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int locations = l.side*l.side;
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int i,j;
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for(i = 0; i < l.batch*locations; ++i){
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for(j = 0; j < l.n; ++j){
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int in_index = i*l.n*l.coords + j*l.coords;
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int out_index = i*l.n*5 + j*5;
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float prob = state.input[in_index+0];
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float x = state.input[in_index+1];
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float y = state.input[in_index+2];
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float w = state.input[in_index+3];
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float h = state.input[in_index+4];
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/*
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float min_w = state.input[in_index+5];
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float max_w = state.input[in_index+6];
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float min_h = state.input[in_index+7];
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float max_h = state.input[in_index+8];
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*/
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l.output[out_index+0] = prob;
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l.output[out_index+1] = x;
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l.output[out_index+2] = y;
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l.output[out_index+3] = w;
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l.output[out_index+4] = h;
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}
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}
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if(state.train){
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float avg_iou = 0;
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int count = 0;
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*(l.cost) = 0;
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int size = l.inputs * l.batch;
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memset(l.delta, 0, size * sizeof(float));
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for (i = 0; i < l.batch*locations; ++i) {
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for(j = 0; j < l.n; ++j){
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int in_index = i*l.n*l.coords + j*l.coords;
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l.delta[in_index+0] = .1*(0-state.input[in_index+0]);
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}
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int truth_index = i*5;
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int best_index = -1;
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float best_iou = 0;
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float best_rmse = 4;
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int bg = !state.truth[truth_index];
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if(bg) continue;
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box truth = {state.truth[truth_index+1], state.truth[truth_index+2], state.truth[truth_index+3], state.truth[truth_index+4]};
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truth.x /= l.side;
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truth.y /= l.side;
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for(j = 0; j < l.n; ++j){
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int out_index = i*l.n*5 + j*5;
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box out = {l.output[out_index+1], l.output[out_index+2], l.output[out_index+3], l.output[out_index+4]};
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//printf("\n%f %f %f %f %f\n", l.output[out_index+0], out.x, out.y, out.w, out.h);
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out.x /= l.side;
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out.y /= l.side;
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float iou = box_iou(out, truth);
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float rmse = box_rmse(out, truth);
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if(best_iou > 0 || iou > 0){
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if(iou > best_iou){
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best_iou = iou;
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best_index = j;
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}
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}else{
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if(rmse < best_rmse){
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best_rmse = rmse;
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best_index = j;
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}
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}
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}
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printf("%d", best_index);
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//int out_index = i*l.n*5 + best_index*5;
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//box out = {l.output[out_index+1], l.output[out_index+2], l.output[out_index+3], l.output[out_index+4]};
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int in_index = i*l.n*l.coords + best_index*l.coords;
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l.delta[in_index+0] = (1-state.input[in_index+0]);
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l.delta[in_index+1] = state.truth[truth_index+1] - state.input[in_index+1];
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l.delta[in_index+2] = state.truth[truth_index+2] - state.input[in_index+2];
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l.delta[in_index+3] = state.truth[truth_index+3] - state.input[in_index+3];
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l.delta[in_index+4] = state.truth[truth_index+4] - state.input[in_index+4];
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/*
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l.delta[in_index+5] = 0 - state.input[in_index+5];
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l.delta[in_index+6] = 1 - state.input[in_index+6];
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l.delta[in_index+7] = 0 - state.input[in_index+7];
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l.delta[in_index+8] = 1 - state.input[in_index+8];
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*/
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/*
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float x = state.input[in_index+1];
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float y = state.input[in_index+2];
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float w = state.input[in_index+3];
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float h = state.input[in_index+4];
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float min_w = state.input[in_index+5];
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float max_w = state.input[in_index+6];
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float min_h = state.input[in_index+7];
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float max_h = state.input[in_index+8];
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*/
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avg_iou += best_iou;
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++count;
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}
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printf("\nAvg IOU: %f %d\n", avg_iou/count, count);
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}
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}
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void backward_region_layer(const region_layer l, network_state state)
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{
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axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
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//copy_cpu(l.batch*l.inputs, l.delta, 1, state.delta, 1);
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}
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#ifdef GPU
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void forward_region_layer_gpu(const region_layer l, network_state state)
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{
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float *in_cpu = calloc(l.batch*l.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(l.batch*l.outputs, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, l.batch*l.outputs);
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}
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cuda_pull_array(state.input, in_cpu, l.batch*l.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_region_layer(l, cpu_state);
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cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
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cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs);
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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_region_layer_gpu(region_layer l, network_state state)
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{
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axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1);
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//copy_ongpu(l.batch*l.inputs, l.delta_gpu, 1, state.delta, 1);
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}
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#endif
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