#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 "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_detection_layer_locations(detection_layer l)
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{
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return l.inputs / (l.classes+l.coords+l.joint+(l.background || l.objectness));
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}
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int get_detection_layer_output_size(detection_layer l)
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{
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return get_detection_layer_locations(l)*((l.background || l.objectness) + l.classes + l.coords);
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}
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detection_layer make_detection_layer(int batch, int inputs, int classes, int coords, int joint, int rescore, int background, int objectness)
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{
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detection_layer l = {0};
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l.type = DETECTION;
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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.objectness = objectness;
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l.background = background;
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l.joint = joint;
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l.cost = calloc(1, sizeof(float));
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l.does_cost=1;
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int outputs = get_detection_layer_output_size(l);
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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(l.output, batch*outputs);
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l.delta_gpu = cuda_make_array(l.delta, 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 l;
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}
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void forward_detection_layer(const detection_layer l, 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(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 mask = (!state.truth || state.truth[out_i + (l.background || l.objectness) + l.classes + 2]);
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float scale = 1;
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if(l.joint) scale = state.input[in_i++];
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else if(l.objectness){
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l.output[out_i++] = 1-state.input[in_i++];
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scale = mask;
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}
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else if(l.background) l.output[out_i++] = scale*state.input[in_i++];
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for(j = 0; j < l.classes; ++j){
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l.output[out_i++] = scale*state.input[in_i++];
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}
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if(l.objectness){
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}else if(l.background){
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softmax_array(l.output + out_i - l.classes-l.background, l.classes+l.background, l.output + out_i - l.classes-l.background);
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activate_array(state.input+in_i, l.coords, LOGISTIC);
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}
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for(j = 0; j < l.coords; ++j){
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l.output[out_i++] = mask*state.input[in_i++];
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}
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}
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float avg_iou = 0;
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int count = 0;
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if(l.does_cost && state.train){
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*(l.cost) = 0;
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int size = get_detection_layer_output_size(l) * 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 classes = (l.objectness || l.background)+l.classes;
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int offset = i*(classes+l.coords);
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for (j = offset; j < offset+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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if(l.background && j == offset) l.delta[j] *= .1;
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}
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box truth;
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truth.x = state.truth[j+0]/7;
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truth.y = state.truth[j+1]/7;
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truth.w = pow(state.truth[j+2], 2);
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truth.h = pow(state.truth[j+3], 2);
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box out;
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out.x = l.output[j+0]/7;
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out.y = l.output[j+1]/7;
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out.w = pow(l.output[j+2], 2);
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out.h = pow(l.output[j+3], 2);
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if(!(truth.w*truth.h)) continue;
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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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*(l.cost) += pow((1-iou), 2);
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l.delta[j+0] = 4 * (state.truth[j+0] - l.output[j+0]);
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l.delta[j+1] = 4 * (state.truth[j+1] - l.output[j+1]);
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l.delta[j+2] = 4 * (state.truth[j+2] - l.output[j+2]);
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l.delta[j+3] = 4 * (state.truth[j+3] - l.output[j+3]);
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if(l.rescore){
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if(l.objectness){
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state.truth[offset] = iou;
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l.delta[offset] = state.truth[offset] - l.output[offset];
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}
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else{
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for (j = offset; j < offset+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_detection_layer(const detection_layer l, network_state state)
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{
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int locations = get_detection_layer_locations(l);
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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 < l.batch*locations; ++i){
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float scale = 1;
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float latent_delta = 0;
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if(l.joint) scale = state.input[in_i++];
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else if (l.objectness) state.delta[in_i++] += -l.delta[out_i++];
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else if (l.background) state.delta[in_i++] += scale*l.delta[out_i++];
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for(j = 0; j < l.classes; ++j){
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latent_delta += state.input[in_i]*l.delta[out_i];
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state.delta[in_i++] += scale*l.delta[out_i++];
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}
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if (l.objectness) {
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}else if (l.background) gradient_array(l.output + out_i, l.coords, LOGISTIC, l.delta + out_i);
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for (j = 0; j < l.coords; ++j){
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state.delta[in_i++] += l.delta[out_i++];
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}
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if(l.joint) state.delta[in_i-l.coords-l.classes-l.joint] += 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 l, network_state state)
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{
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int outputs = get_detection_layer_output_size(l);
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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*outputs, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, l.batch*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_detection_layer(l, cpu_state);
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cuda_push_array(l.output_gpu, l.output, l.batch*outputs);
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cuda_push_array(l.delta_gpu, l.delta, l.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 l, network_state state)
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{
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int outputs = get_detection_layer_output_size(l);
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float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
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float *delta_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*outputs, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, l.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, l.batch*l.inputs);
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cuda_pull_array(state.delta, delta_cpu, l.batch*l.inputs);
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cuda_pull_array(l.delta_gpu, l.delta, l.batch*outputs);
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backward_detection_layer(l, cpu_state);
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cuda_push_array(state.delta, delta_cpu, l.batch*l.inputs);
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if (truth_cpu) free(truth_cpu);
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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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