mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
route handles input images well....ish
This commit is contained in:
@ -8,47 +8,49 @@
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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 layer)
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int get_detection_layer_locations(detection_layer l)
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{
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return layer.inputs / (layer.classes+layer.coords+layer.rescore+layer.background);
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return l.inputs / (l.classes+l.coords+l.rescore+l.background);
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}
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int get_detection_layer_output_size(detection_layer layer)
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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(layer)*(layer.background + layer.classes + layer.coords);
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return get_detection_layer_locations(l)*(l.background + 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 rescore, int background, int nuisance)
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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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detection_layer l = {0};
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l.type = DETECTION;
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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->cost = calloc(1, sizeof(float));
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layer->does_cost=1;
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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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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.nuisance = nuisance;
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l.cost = calloc(1, sizeof(float));
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l.does_cost=1;
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l.background = background;
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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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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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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, "Detection Layer\n");
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srand(0);
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return layer;
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return l;
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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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void dark_zone(detection_layer l, 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 index = start+l.background+class;
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int size = l.classes+l.coords+l.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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@ -60,9 +62,9 @@ void dark_zone(detection_layer layer, int class, int start, network_state state)
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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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l.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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//l.output[start + di] = 1;
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}
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}
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}
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@ -299,47 +301,47 @@ dbox diou(box a, box b)
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return dd;
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}
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void forward_detection_layer(const detection_layer layer, network_state state)
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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(layer);
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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 < 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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for(i = 0; i < l.batch*locations; ++i){
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int mask = (!state.truth || state.truth[out_i + l.background + l.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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if(l.rescore) scale = state.input[in_i++];
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else if(l.nuisance){
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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(layer.background) layer.output[out_i++] = scale*state.input[in_i++];
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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 < layer.classes; ++j){
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layer.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(layer.nuisance){
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if(l.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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}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 < layer.coords; ++j){
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layer.output[out_i++] = mask*state.input[in_i++];
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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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if(layer.does_cost && state.train && 0){
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if(l.does_cost && state.train && 0){
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int count = 0;
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float avg = 0;
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*(layer.cost) = 0;
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int size = get_detection_layer_output_size(layer) * layer.batch;
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memset(layer.delta, 0, size * sizeof(float));
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for (i = 0; i < layer.batch*locations; ++i) {
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int classes = layer.nuisance+layer.classes;
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int offset = i*(classes+layer.coords);
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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.nuisance+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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*(layer.cost) += pow(state.truth[j] - layer.output[j], 2);
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layer.delta[j] = state.truth[j] - layer.output[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 truth;
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truth.x = state.truth[j+0];
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@ -347,17 +349,17 @@ void forward_detection_layer(const detection_layer layer, network_state state)
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truth.w = state.truth[j+2];
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truth.h = state.truth[j+3];
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box out;
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out.x = layer.output[j+0];
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out.y = layer.output[j+1];
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out.w = layer.output[j+2];
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out.h = layer.output[j+3];
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out.x = l.output[j+0];
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out.y = l.output[j+1];
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out.w = l.output[j+2];
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out.h = l.output[j+3];
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if(!(truth.w*truth.h)) continue;
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//printf("iou: %f\n", iou);
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dbox d = diou(out, truth);
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layer.delta[j+0] = d.dx;
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layer.delta[j+1] = d.dy;
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layer.delta[j+2] = d.dw;
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layer.delta[j+3] = d.dh;
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l.delta[j+0] = d.dx;
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l.delta[j+1] = d.dy;
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l.delta[j+2] = d.dw;
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l.delta[j+3] = d.dh;
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int sqr = 1;
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if(sqr){
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@ -367,7 +369,7 @@ void forward_detection_layer(const detection_layer layer, network_state state)
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out.h *= out.h;
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}
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float iou = box_iou(truth, out);
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*(layer.cost) += pow((1-iou), 2);
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*(l.cost) += pow((1-iou), 2);
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avg += iou;
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++count;
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}
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@ -375,24 +377,24 @@ void forward_detection_layer(const detection_layer layer, network_state state)
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}
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/*
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int count = 0;
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for(i = 0; i < layer.batch*locations; ++i){
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for(j = 0; j < layer.classes+layer.background; ++j){
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printf("%f, ", layer.output[count++]);
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for(i = 0; i < l.batch*locations; ++i){
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for(j = 0; j < l.classes+l.background; ++j){
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printf("%f, ", l.output[count++]);
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}
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printf("\n");
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for(j = 0; j < layer.coords; ++j){
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printf("%f, ", layer.output[count++]);
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for(j = 0; j < l.coords; ++j){
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printf("%f, ", l.output[count++]);
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}
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printf("\n");
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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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if(l.background || 1){
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for(i = 0; i < l.batch*locations; ++i){
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int index = i*(l.classes+l.coords+l.background);
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for(j= 0; j < l.classes; ++j){
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if(state.truth[index+j+l.background]){
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//dark_zone(l, j, index, state);
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}
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}
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}
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@ -400,66 +402,66 @@ void forward_detection_layer(const detection_layer layer, network_state state)
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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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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(layer);
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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 < layer.batch*locations; ++i){
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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(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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if(l.rescore) scale = state.input[in_i++];
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else if (l.nuisance) 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 (layer.nuisance) {
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if (l.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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}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(layer.rescore) state.delta[in_i-layer.coords-layer.classes-layer.rescore-layer.background] = latent_delta;
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if(l.rescore) state.delta[in_i-l.coords-l.classes-l.rescore-l.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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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(layer);
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float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
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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(layer.batch*outputs, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, layer.batch*outputs);
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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, layer.batch*layer.inputs);
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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(layer, cpu_state);
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cuda_push_array(layer.output_gpu, layer.output, layer.batch*outputs);
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cuda_push_array(layer.delta_gpu, layer.delta, layer.batch*outputs);
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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 layer, network_state state)
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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(layer);
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int outputs = get_detection_layer_output_size(l);
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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 *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(layer.batch*outputs, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, layer.batch*outputs);
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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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@ -467,10 +469,10 @@ void backward_detection_layer_gpu(detection_layer layer, network_state state)
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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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cuda_pull_array(state.input, in_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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free(in_cpu);
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free(delta_cpu);
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