#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 w, int h, int n, int classes, int coords)
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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.h = h;
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l.w = w;
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l.classes = classes;
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l.coords = coords;
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l.cost = calloc(1, sizeof(float));
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l.outputs = h*w*n*(classes + coords + 1);
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l.inputs = l.outputs;
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l.truths = 30*(5);
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l.delta = calloc(batch*l.outputs, sizeof(float));
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l.output = calloc(batch*l.outputs, sizeof(float));
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#ifdef GPU
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l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
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l.delta_gpu = cuda_make_array(l.delta, batch*l.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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box get_region_box2(float *x, int index, int i, int j, int w, int h)
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{
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float aspect = exp(x[index+0]);
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float scale = logistic_activate(x[index+1]);
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float move_x = x[index+2];
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float move_y = x[index+3];
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box b;
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b.w = sqrt(scale * aspect);
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b.h = b.w * 1./aspect;
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b.x = move_x * b.w + (i + .5)/w;
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b.y = move_y * b.h + (j + .5)/h;
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return b;
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}
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float delta_region_box2(box truth, float *output, int index, int i, int j, int w, int h, float *delta)
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{
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box pred = get_region_box2(output, index, i, j, w, h);
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float iou = box_iou(pred, truth);
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float true_aspect = truth.w/truth.h;
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float true_scale = truth.w*truth.h;
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float true_dx = (truth.x - (i+.5)/w) / truth.w;
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float true_dy = (truth.y - (j+.5)/h) / truth.h;
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delta[index + 0] = (true_aspect - exp(output[index + 0])) * exp(output[index + 0]);
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delta[index + 1] = (true_scale - logistic_activate(output[index + 1])) * logistic_gradient(logistic_activate(output[index + 1]));
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delta[index + 2] = true_dx - output[index + 2];
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delta[index + 3] = true_dy - output[index + 3];
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return iou;
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}
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box get_region_box(float *x, int index, int i, int j, int w, int h, int adjust, int logistic)
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{
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box b;
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b.x = (x[index + 0] + i + .5)/w;
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b.y = (x[index + 1] + j + .5)/h;
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b.w = x[index + 2];
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b.h = x[index + 3];
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if(logistic){
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b.w = logistic_activate(x[index + 2]);
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b.h = logistic_activate(x[index + 3]);
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}
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//if(adjust && b.w < .01) b.w = .01;
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//if(adjust && b.h < .01) b.h = .01;
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return b;
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}
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float delta_region_box(box truth, float *output, int index, int i, int j, int w, int h, float *delta, int logistic, float scale)
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{
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box pred = get_region_box(output, index, i, j, w, h, 0, logistic);
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float iou = box_iou(pred, truth);
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delta[index + 0] = scale * (truth.x - pred.x);
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delta[index + 1] = scale * (truth.y - pred.y);
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delta[index + 2] = scale * ((truth.w - pred.w)*(logistic ? logistic_gradient(pred.w) : 1));
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delta[index + 3] = scale * ((truth.h - pred.h)*(logistic ? logistic_gradient(pred.h) : 1));
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return iou;
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}
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float logit(float x)
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{
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return log(x/(1.-x));
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}
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float tisnan(float x)
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{
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return (x != x);
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}
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#define LOG 1
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void forward_region_layer(const region_layer l, network_state state)
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{
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int i,j,b,t,n;
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int size = l.coords + l.classes + 1;
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memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
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reorg(l.output, l.w*l.h, size*l.n, l.batch, 1);
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for (b = 0; b < l.batch; ++b){
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for(i = 0; i < l.h*l.w*l.n; ++i){
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int index = size*i + b*l.outputs;
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l.output[index + 4] = logistic_activate(l.output[index + 4]);
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if(l.softmax){
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softmax_array(l.output + index + 5, l.classes, 1, l.output + index + 5);
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}
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}
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}
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if(!state.train) return;
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memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
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float avg_iou = 0;
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float avg_cat = 0;
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float avg_obj = 0;
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float avg_anyobj = 0;
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int count = 0;
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*(l.cost) = 0;
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for (b = 0; b < l.batch; ++b) {
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for (j = 0; j < l.h; ++j) {
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for (i = 0; i < l.w; ++i) {
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for (n = 0; n < l.n; ++n) {
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int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
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box pred = get_region_box(l.output, index, i, j, l.w, l.h, 1, LOG);
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float best_iou = 0;
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for(t = 0; t < 30; ++t){
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box truth = float_to_box(state.truth + t*5 + b*l.truths);
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if(!truth.x) break;
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float iou = box_iou(pred, truth);
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if (iou > best_iou) best_iou = iou;
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}
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avg_anyobj += l.output[index + 4];
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l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
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if(best_iou > .5) l.delta[index + 4] = 0;
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/*
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if(*(state.net.seen) < 6400){
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box truth = {0};
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truth.x = (i + .5)/l.w;
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truth.y = (j + .5)/l.h;
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truth.w = .5;
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truth.h = .5;
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delta_region_box(truth, l.output, index, i, j, l.w, l.h, l.delta, LOG, 1);
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}
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*/
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}
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}
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}
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for(t = 0; t < 30; ++t){
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box truth = float_to_box(state.truth + t*5 + b*l.truths);
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int class = state.truth[t*5 + b*l.truths + 4];
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if(!truth.x) break;
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float best_iou = 0;
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int best_index = 0;
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int best_n = 0;
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i = (truth.x * l.w);
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j = (truth.y * l.h);
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//printf("%d %f %d %f\n", i, truth.x*l.w, j, truth.y*l.h);
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box truth_shift = truth;
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truth_shift.x = 0;
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truth_shift.y = 0;
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printf("index %d %d\n",i, j);
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for(n = 0; n < l.n; ++n){
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int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
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box pred = get_region_box(l.output, index, i, j, l.w, l.h, 1, LOG);
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printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h);
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pred.x = 0;
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pred.y = 0;
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float iou = box_iou(pred, truth_shift);
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if (iou > best_iou){
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best_index = index;
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best_iou = iou;
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best_n = n;
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}
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}
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printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x, truth.y, truth.w, truth.h);
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float iou = delta_region_box(truth, l.output, best_index, i, j, l.w, l.h, l.delta, LOG, l.coord_scale);
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avg_iou += iou;
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//l.delta[best_index + 4] = iou - l.output[best_index + 4];
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avg_obj += l.output[best_index + 4];
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l.delta[best_index + 4] = l.object_scale * (1 - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
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if (l.rescore) {
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l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
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}
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//printf("%f\n", l.delta[best_index+1]);
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/*
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if(isnan(l.delta[best_index+1])){
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printf("%f\n", true_scale);
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printf("%f\n", l.output[best_index + 1]);
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printf("%f\n", truth.w);
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printf("%f\n", truth.h);
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error("bad");
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}
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*/
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for(n = 0; n < l.classes; ++n){
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l.delta[best_index + 5 + n] = l.class_scale * (((n == class)?1 : 0) - l.output[best_index + 5 + n]);
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if(n == class) avg_cat += l.output[best_index + 5 + n];
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}
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/*
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if(0){
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printf("truth: %f %f %f %f\n", truth.x, truth.y, truth.w, truth.h);
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printf("pred: %f %f %f %f\n\n", pred.x, pred.y, pred.w, pred.h);
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float aspect = exp(true_aspect);
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float scale = logistic_activate(true_scale);
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float move_x = true_dx;
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float move_y = true_dy;
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box b;
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b.w = sqrt(scale * aspect);
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b.h = b.w * 1./aspect;
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b.x = move_x * b.w + (i + .5)/l.w;
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b.y = move_y * b.h + (j + .5)/l.h;
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printf("%f %f\n", b.x, truth.x);
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printf("%f %f\n", b.y, truth.y);
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printf("%f %f\n", b.w, truth.w);
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printf("%f %f\n", b.h, truth.h);
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//printf("%f\n", box_iou(b, truth));
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}
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*/
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++count;
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}
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}
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printf("\n");
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reorg(l.delta, l.w*l.h, size*l.n, l.batch, 0);
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*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
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printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), count);
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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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}
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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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/*
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if(!state.train){
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copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1);
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return;
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}
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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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int num_truth = l.batch*l.truths;
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truth_cpu = calloc(num_truth, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, num_truth);
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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 = 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.outputs, 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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