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stuff changed probably

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This commit is contained in:
Joseph Redmon 2015-03-24 13:20:56 -07:00
parent 7100de0b59
commit 56b6561ae4
7 changed files with 43 additions and 25 deletions
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12
src/data.c
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@ -137,18 +137,20 @@ void fill_truth_detection(char *path, float *truth, int classes, int height, int
if(j < 0) j = 0;
if(j >= num_height) j = num_height-1;
float dw = (x - i*box_width)/box_width;
float dh = (y - j*box_height)/box_height;
float dw = constrain(0,1, (x - i*box_width)/box_width );
float dh = constrain(0,1, (y - j*box_height)/box_height );
float th = constrain(0,1, h*(height+jitter)/height );
float tw = constrain(0,1, w*(width+jitter)/width );
int index = (i+j*num_width)*(4+classes+background);
if(truth[index+classes+background]) continue;
if(truth[index+classes+background+2]) continue;
if(background) truth[index++] = 0;
truth[index+id] = 1;
index += classes;
truth[index++] = dh;
truth[index++] = dw;
truth[index++] = h*(height+jitter)/height;
truth[index++] = w*(width+jitter)/width;
truth[index++] = th;
truth[index++] = tw;
}
free(boxes);
}

19
src/detection.c
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@ -50,7 +50,7 @@ void train_detection(char *cfgfile, char *weightfile)
if(weightfile){
load_weights(&net, weightfile);
}
net.seen = 0;
//net.seen = 0;
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 128;
srand(time(0));
@ -63,7 +63,7 @@ void train_detection(char *cfgfile, char *weightfile)
int im_dim = 512;
int jitter = 64;
int classes = 20;
int background = 0;
int background = 1;
pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, background, &buffer);
clock_t time;
while(1){
@ -109,8 +109,9 @@ void validate_detection(char *cfgfile, char *weightfile)
char **paths = (char **)list_to_array(plist);
int im_size = 448;
int classes = 20;
int background = 0;
int num_output = 7*7*(4+classes+background);
int background = 1;
int nuisance = 0;
int num_output = 7*7*(4+classes+background+nuisance);
int m = plist->size;
int i = 0;
@ -134,17 +135,19 @@ void validate_detection(char *cfgfile, char *weightfile)
matrix pred = network_predict_data(net, val);
int j, k, class;
for(j = 0; j < pred.rows; ++j){
for(k = 0; k < pred.cols; k += classes+4+background){
for(k = 0; k < pred.cols; k += classes+4+background+nuisance){
float scale = 1.;
if(nuisance) scale = pred.vals[j][k];
for(class = 0; class < classes; ++class){
int index = (k)/(classes+4+background);
int index = (k)/(classes+4+background+nuisance);
int r = index/7;
int c = index%7;
int ci = k+classes+background;
int ci = k+classes+background+nuisance;
float y = (r + pred.vals[j][ci + 0])/7.;
float x = (c + pred.vals[j][ci + 1])/7.;
float h = pred.vals[j][ci + 2];
float w = pred.vals[j][ci + 3];
printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, pred.vals[j][k+class+background], y, x, h, w);
printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, scale*pred.vals[j][k+class+background+nuisance], y, x, h, w);
}
}
}

21
src/detection_layer.c
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@ -16,7 +16,7 @@ int get_detection_layer_output_size(detection_layer layer)
return get_detection_layer_locations(layer)*(layer.background + layer.classes + layer.coords);
}
detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background)
detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background, int nuisance)
{
detection_layer *layer = calloc(1, sizeof(detection_layer));
@ -25,6 +25,7 @@ detection_layer *make_detection_layer(int batch, int inputs, int classes, int co
layer->classes = classes;
layer->coords = coords;
layer->rescore = rescore;
layer->nuisance = nuisance;
layer->background = background;
int outputs = get_detection_layer_output_size(*layer);
layer->output = calloc(batch*outputs, sizeof(float));
@ -72,12 +73,18 @@ void forward_detection_layer(const detection_layer layer, network_state state)
int mask = (!state.truth || state.truth[out_i + layer.background + layer.classes + 2]);
float scale = 1;
if(layer.rescore) scale = state.input[in_i++];
if(layer.background) layer.output[out_i++] = scale*state.input[in_i++];
else if(layer.nuisance){
layer.output[out_i++] = 1-state.input[in_i++];
scale = mask;
}
else if(layer.background) layer.output[out_i++] = scale*state.input[in_i++];
for(j = 0; j < layer.classes; ++j){
layer.output[out_i++] = scale*state.input[in_i++];
}
if(layer.background){
if(layer.nuisance){
}else if(layer.background){
softmax_array(layer.output + out_i - layer.classes-layer.background, layer.classes+layer.background, layer.output + out_i - layer.classes-layer.background);
activate_array(state.input+in_i, layer.coords, LOGISTIC);
}
@ -85,6 +92,7 @@ void forward_detection_layer(const detection_layer layer, network_state state)
layer.output[out_i++] = mask*state.input[in_i++];
}
}
/*
if(layer.background || 1){
for(i = 0; i < layer.batch*locations; ++i){
int index = i*(layer.classes+layer.coords+layer.background);
@ -95,6 +103,7 @@ void forward_detection_layer(const detection_layer layer, network_state state)
}
}
}
*/
}
void backward_detection_layer(const detection_layer layer, network_state state)
@ -107,13 +116,15 @@ void backward_detection_layer(const detection_layer layer, network_state state)
float scale = 1;
float latent_delta = 0;
if(layer.rescore) scale = state.input[in_i++];
if(layer.background) state.delta[in_i++] = scale*layer.delta[out_i++];
else if (layer.nuisance) state.delta[in_i++] = -layer.delta[out_i++];
else if (layer.background) state.delta[in_i++] = scale*layer.delta[out_i++];
for(j = 0; j < layer.classes; ++j){
latent_delta += state.input[in_i]*layer.delta[out_i];
state.delta[in_i++] = scale*layer.delta[out_i++];
}
if (layer.background) gradient_array(layer.output + out_i, layer.coords, LOGISTIC, layer.delta + out_i);
if (layer.nuisance) ;
else if (layer.background) gradient_array(layer.output + out_i, layer.coords, LOGISTIC, layer.delta + out_i);
for(j = 0; j < layer.coords; ++j){
state.delta[in_i++] = layer.delta[out_i++];
}

3
src/detection_layer.h
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@ -10,6 +10,7 @@ typedef struct {
int coords;
int background;
int rescore;
int nuisance;
float *output;
float *delta;
#ifdef GPU
@ -18,7 +19,7 @@ typedef struct {
#endif
} detection_layer;
detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background);
detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background, int nuisance);
void forward_detection_layer(const detection_layer layer, network_state state);
void backward_detection_layer(const detection_layer layer, network_state state);
int get_detection_layer_output_size(detection_layer layer);

5
src/parser.c
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@ -165,8 +165,9 @@ detection_layer *parse_detection(list *options, size_params params)
int coords = option_find_int(options, "coords", 1);
int classes = option_find_int(options, "classes", 1);
int rescore = option_find_int(options, "rescore", 1);
int nuisance = option_find_int(options, "nuisance", 0);
int background = option_find_int(options, "background", 1);
detection_layer *layer = make_detection_layer(params.batch, params.inputs, classes, coords, rescore, background);
detection_layer *layer = make_detection_layer(params.batch, params.inputs, classes, coords, rescore, background, nuisance);
option_unused(options);
return layer;
}
@ -550,7 +551,7 @@ void print_softmax_cfg(FILE *fp, softmax_layer *l, network net, int count)
void print_detection_cfg(FILE *fp, detection_layer *l, network net, int count)
{
fprintf(fp, "[detection]\n");
fprintf(fp, "classes=%d\ncoords=%d\nrescore=%d\n", l->classes, l->coords, l->rescore);
fprintf(fp, "classes=%d\ncoords=%d\nrescore=%d\nnuisance=%d\n", l->classes, l->coords, l->rescore, l->nuisance);
fprintf(fp, "\n");
}

6
src/utils.c
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@ -276,10 +276,10 @@ float variance_array(float *a, int n)
return variance;
}
float constrain(float a, float max)
float constrain(float min, float max, float a)
{
if(a > abs(max)) return abs(max);
if(a < -abs(max)) return -abs(max);
if (a < min) return min;
if (a > max) return max;
return a;
}

2
src/utils.h
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@ -26,7 +26,7 @@ void normalize_array(float *a, int n);
void scale_array(float *a, int n, float s);
void translate_array(float *a, int n, float s);
int max_index(float *a, int n);
float constrain(float a, float max);
float constrain(float min, float max, float a);
float mse_array(float *a, int n);
float rand_normal();
float rand_uniform();