#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 <string.h>
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#include <stdlib.h>
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int get_region_layer_locations(region_layer l)
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{
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return l.inputs / (l.classes+l.coords);
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}
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region_layer make_region_layer(int batch, int inputs, int n, 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.cost = calloc(1, sizeof(float));
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int outputs = inputs;
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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*outputs, sizeof(float));
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#ifdef GPU
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l.output_gpu = cuda_make_array(0, batch*outputs);
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l.delta_gpu = cuda_make_array(0, batch*outputs);
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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 = get_region_layer_locations(l);
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int i,j;
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for(i = 0; i < l.batch*locations; ++i){
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int index = i*(l.classes + l.coords);
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int mask = (!state.truth || !state.truth[index]);
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for(j = 0; j < l.classes; ++j){
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l.output[index+j] = state.input[index+j];
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}
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softmax_array(l.output + index, l.classes, l.output + index);
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index += l.classes;
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for(j = 0; j < l.coords; ++j){
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l.output[index+j] = mask*state.input[index+j];
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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.outputs * 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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int offset = i*(l.classes+l.coords);
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int bg = state.truth[offset];
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for (j = offset; j < offset+l.classes; ++j) {
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//*(l.cost) += pow(state.truth[j] - l.output[j], 2);
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//l.delta[j] = state.truth[j] - l.output[j];
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}
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box anchor = {0,0,.5,.5};
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box truth_code = {state.truth[j+0], state.truth[j+1], state.truth[j+2], state.truth[j+3]};
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box out_code = {l.output[j+0], l.output[j+1], l.output[j+2], l.output[j+3]};
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box out = decode_box(out_code, anchor);
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box truth = decode_box(truth_code, anchor);
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if(bg) continue;
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//printf("Box: %f %f %f %f\n", truth.x, truth.y, truth.w, truth.h);
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//printf("Code: %f %f %f %f\n", truth_code.x, truth_code.y, truth_code.w, truth_code.h);
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//printf("Pred : %f %f %f %f\n", out.x, out.y, out.w, out.h);
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// printf("Pred Code: %f %f %f %f\n", out_code.x, out_code.y, out_code.w, out_code.h);
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float iou = box_iou(out, truth);
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avg_iou += iou;
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++count;
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/*
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*(l.cost) += pow((1-iou), 2);
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l.delta[j+0] = (state.truth[j+0] - l.output[j+0]);
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l.delta[j+1] = (state.truth[j+1] - l.output[j+1]);
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l.delta[j+2] = (state.truth[j+2] - l.output[j+2]);
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l.delta[j+3] = (state.truth[j+3] - l.output[j+3]);
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*/
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for (j = offset+l.classes; j < offset+l.classes+l.coords; ++j) {
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//*(l.cost) += pow(state.truth[j] - l.output[j], 2);
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//l.delta[j] = state.truth[j] - l.output[j];
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float diff = state.truth[j] - l.output[j];
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if (fabs(diff) < 1){
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l.delta[j] = diff;
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*(l.cost) += .5*pow(state.truth[j] - l.output[j], 2);
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} else {
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l.delta[j] = (diff > 0) ? 1 : -1;
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*(l.cost) += fabs(diff) - .5;
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}
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//l.delta[j] = state.truth[j] - l.output[j];
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}
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/*
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if(l.rescore){
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for (j = offset; j < offset+l.classes; ++j) {
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if(state.truth[j]) state.truth[j] = iou;
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l.delta[j] = state.truth[j] - l.output[j];
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}
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}
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*/
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}
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printf("Avg IOU: %f\n", avg_iou/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_gpu, 1, state.delta, 1);
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//copy_cpu(l.batch*l.inputs, l.delta_gpu, 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.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_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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