From b5938098d12d5c9fe48e7cc71ae3d75b7306833f Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Mon, 02 Jan 2017 12:33:31 +0000
Subject: [PATCH] Update Readme.md - pragma-libs in How to compile
---
src/region_layer.c | 241 ++++++++++++++++++++++++++++++++++-------------
1 files changed, 172 insertions(+), 69 deletions(-)
diff --git a/src/region_layer.c b/src/region_layer.c
index 2702636..9095b3c 100644
--- a/src/region_layer.c
+++ b/src/region_layer.c
@@ -9,6 +9,8 @@
#include <string.h>
#include <stdlib.h>
+#define DOABS 1
+
region_layer make_region_layer(int batch, int w, int h, int n, int classes, int coords)
{
region_layer l = {0};
@@ -42,25 +44,43 @@
l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
#endif
- fprintf(stderr, "Region Layer\n");
+ fprintf(stderr, "detection\n");
srand(0);
return l;
}
-#define LOG 1
+void resize_region_layer(layer *l, int w, int h)
+{
+ l->w = w;
+ l->h = h;
+
+ l->outputs = h*w*l->n*(l->classes + l->coords + 1);
+ l->inputs = l->outputs;
+
+ l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
+ l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float));
+
+#ifdef GPU
+ cuda_free(l->delta_gpu);
+ cuda_free(l->output_gpu);
+
+ l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs);
+ l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs);
+#endif
+}
box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h)
{
box b;
- b.x = (i + .5)/w + x[index + 0] * biases[2*n];
- b.y = (j + .5)/h + x[index + 1] * biases[2*n + 1];
- if(LOG){
- b.x = (i + logistic_activate(x[index + 0])) / w;
- b.y = (j + logistic_activate(x[index + 1])) / h;
- }
+ b.x = (i + logistic_activate(x[index + 0])) / w;
+ b.y = (j + logistic_activate(x[index + 1])) / h;
b.w = exp(x[index + 2]) * biases[2*n];
b.h = exp(x[index + 3]) * biases[2*n+1];
+ if(DOABS){
+ b.w = exp(x[index + 2]) * biases[2*n] / w;
+ b.h = exp(x[index + 3]) * biases[2*n+1] / h;
+ }
return b;
}
@@ -69,26 +89,47 @@
box pred = get_region_box(x, biases, n, index, i, j, w, h);
float iou = box_iou(pred, truth);
- float tx = (truth.x - (i + .5)/w) / biases[2*n];
- float ty = (truth.y - (j + .5)/h) / biases[2*n + 1];
- if(LOG){
- tx = (truth.x*w - i);
- ty = (truth.y*h - j);
- }
+ float tx = (truth.x*w - i);
+ float ty = (truth.y*h - j);
float tw = log(truth.w / biases[2*n]);
float th = log(truth.h / biases[2*n + 1]);
-
- delta[index + 0] = scale * (tx - x[index + 0]);
- delta[index + 1] = scale * (ty - x[index + 1]);
- if(LOG){
- delta[index + 0] = scale * (tx - logistic_activate(x[index + 0])) * logistic_gradient(logistic_activate(x[index + 0]));
- delta[index + 1] = scale * (ty - logistic_activate(x[index + 1])) * logistic_gradient(logistic_activate(x[index + 1]));
+ if(DOABS){
+ tw = log(truth.w*w / biases[2*n]);
+ th = log(truth.h*h / biases[2*n + 1]);
}
+
+ delta[index + 0] = scale * (tx - logistic_activate(x[index + 0])) * logistic_gradient(logistic_activate(x[index + 0]));
+ delta[index + 1] = scale * (ty - logistic_activate(x[index + 1])) * logistic_gradient(logistic_activate(x[index + 1]));
delta[index + 2] = scale * (tw - x[index + 2]);
delta[index + 3] = scale * (th - x[index + 3]);
return iou;
}
+void delta_region_class(float *output, float *delta, int index, int class, int classes, tree *hier, float scale, float *avg_cat)
+{
+ int i, n;
+ if(hier){
+ float pred = 1;
+ while(class >= 0){
+ pred *= output[index + class];
+ int g = hier->group[class];
+ int offset = hier->group_offset[g];
+ for(i = 0; i < hier->group_size[g]; ++i){
+ delta[index + offset + i] = scale * (0 - output[index + offset + i]);
+ }
+ delta[index + class] = scale * (1 - output[index + class]);
+
+ class = hier->parent[class];
+ }
+ *avg_cat += pred;
+ } else {
+ for(n = 0; n < classes; ++n){
+ delta[index + n] = scale * (((n == class)?1 : 0) - output[index + n]);
+ if(n == class) *avg_cat += output[index + n];
+ }
+ }
+}
+
float logit(float x)
{
return log(x/(1.-x));
@@ -105,18 +146,34 @@
int i,j,b,t,n;
int size = l.coords + l.classes + 1;
memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
- reorg(l.output, l.w*l.h, size*l.n, l.batch, 1);
+ #ifndef GPU
+ flatten(l.output, l.w*l.h, size*l.n, l.batch, 1);
+ #endif
for (b = 0; b < l.batch; ++b){
for(i = 0; i < l.h*l.w*l.n; ++i){
int index = size*i + b*l.outputs;
l.output[index + 4] = logistic_activate(l.output[index + 4]);
- if(l.softmax_tree){
+ }
+ }
+
+
+#ifndef GPU
+ if (l.softmax_tree){
+ for (b = 0; b < l.batch; ++b){
+ for(i = 0; i < l.h*l.w*l.n; ++i){
+ int index = size*i + b*l.outputs;
softmax_tree(l.output + index + 5, 1, 0, 1, l.softmax_tree, l.output + index + 5);
- } else if(l.softmax){
+ }
+ }
+ } else if (l.softmax){
+ for (b = 0; b < l.batch; ++b){
+ for(i = 0; i < l.h*l.w*l.n; ++i){
+ int index = size*i + b*l.outputs;
softmax(l.output + index + 5, l.classes, 1, l.output + index + 5);
}
}
}
+#endif
if(!state.train) return;
memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
float avg_iou = 0;
@@ -125,23 +182,64 @@
float avg_obj = 0;
float avg_anyobj = 0;
int count = 0;
+ int class_count = 0;
*(l.cost) = 0;
for (b = 0; b < l.batch; ++b) {
+ if(l.softmax_tree){
+ int onlyclass = 0;
+ for(t = 0; t < 30; ++t){
+ box truth = float_to_box(state.truth + t*5 + b*l.truths);
+ if(!truth.x) break;
+ int class = state.truth[t*5 + b*l.truths + 4];
+ float maxp = 0;
+ int maxi = 0;
+ if(truth.x > 100000 && truth.y > 100000){
+ for(n = 0; n < l.n*l.w*l.h; ++n){
+ int index = size*n + b*l.outputs + 5;
+ float scale = l.output[index-1];
+ float p = scale*get_hierarchy_probability(l.output + index, l.softmax_tree, class);
+ if(p > maxp){
+ maxp = p;
+ maxi = n;
+ }
+ }
+ int index = size*maxi + b*l.outputs + 5;
+ delta_region_class(l.output, l.delta, index, class, l.classes, l.softmax_tree, l.class_scale, &avg_cat);
+ ++class_count;
+ onlyclass = 1;
+ break;
+ }
+ }
+ if(onlyclass) continue;
+ }
for (j = 0; j < l.h; ++j) {
for (i = 0; i < l.w; ++i) {
for (n = 0; n < l.n; ++n) {
int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
float best_iou = 0;
+ int best_class = -1;
for(t = 0; t < 30; ++t){
box truth = float_to_box(state.truth + t*5 + b*l.truths);
if(!truth.x) break;
float iou = box_iou(pred, truth);
- if (iou > best_iou) best_iou = iou;
+ if (iou > best_iou) {
+ best_class = state.truth[t*5 + b*l.truths + 4];
+ best_iou = iou;
+ }
}
avg_anyobj += l.output[index + 4];
l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
- if(best_iou > l.thresh) l.delta[index + 4] = 0;
+ if(l.classfix == -1) l.delta[index + 4] = l.noobject_scale * ((best_iou - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
+ else{
+ if (best_iou > l.thresh) {
+ l.delta[index + 4] = 0;
+ if(l.classfix > 0){
+ delta_region_class(l.output, l.delta, index + 5, best_class, l.classes, l.softmax_tree, l.class_scale*(l.classfix == 2 ? l.output[index + 4] : 1), &avg_cat);
+ ++class_count;
+ }
+ }
+ }
if(*(state.net.seen) < 12800){
box truth = {0};
@@ -149,11 +247,11 @@
truth.y = (j + .5)/l.h;
truth.w = l.biases[2*n];
truth.h = l.biases[2*n+1];
+ if(DOABS){
+ truth.w = l.biases[2*n]/l.w;
+ truth.h = l.biases[2*n+1]/l.h;
+ }
delta_region_box(truth, l.output, l.biases, n, index, i, j, l.w, l.h, l.delta, .01);
- //l.delta[index + 0] = .1 * (0 - l.output[index + 0]);
- //l.delta[index + 1] = .1 * (0 - l.output[index + 1]);
- //l.delta[index + 2] = .1 * (0 - l.output[index + 2]);
- //l.delta[index + 3] = .1 * (0 - l.output[index + 3]);
}
}
}
@@ -178,6 +276,10 @@
if(l.bias_match){
pred.w = l.biases[2*n];
pred.h = l.biases[2*n+1];
+ if(DOABS){
+ pred.w = l.biases[2*n]/l.w;
+ pred.h = l.biases[2*n+1]/l.h;
+ }
}
//printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h);
pred.x = 0;
@@ -205,35 +307,17 @@
int class = state.truth[t*5 + b*l.truths + 4];
if (l.map) class = l.map[class];
- if(l.softmax_tree){
- float pred = 1;
- while(class >= 0){
- pred *= l.output[best_index + 5 + class];
- int g = l.softmax_tree->group[class];
- int i;
- int offset = l.softmax_tree->group_offset[g];
- for(i = 0; i < l.softmax_tree->group_size[g]; ++i){
- int index = best_index + 5 + offset + i;
- l.delta[index] = l.class_scale * (0 - l.output[index]);
- }
- l.delta[best_index + 5 + class] = l.class_scale * (1 - l.output[best_index + 5 + class]);
-
- class = l.softmax_tree->parent[class];
- }
- avg_cat += pred;
- } else {
- for(n = 0; n < l.classes; ++n){
- l.delta[best_index + 5 + n] = l.class_scale * (((n == class)?1 : 0) - l.output[best_index + 5 + n]);
- if(n == class) avg_cat += l.output[best_index + 5 + n];
- }
- }
+ delta_region_class(l.output, l.delta, best_index + 5, class, l.classes, l.softmax_tree, l.class_scale, &avg_cat);
++count;
+ ++class_count;
}
}
//printf("\n");
- reorg(l.delta, l.w*l.h, size*l.n, l.batch, 0);
+ #ifndef GPU
+ flatten(l.delta, l.w*l.h, size*l.n, l.batch, 0);
+ #endif
*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
- printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count);
+ printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f, count: %d\n", avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count);
}
void backward_region_layer(const region_layer l, network_state state)
@@ -241,11 +325,10 @@
axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
}
-void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness)
+void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map)
{
int i,j,n;
float *predictions = l.output;
- //int per_cell = 5*num+classes;
for (i = 0; i < l.w*l.h; ++i){
int row = i / l.w;
int col = i % l.w;
@@ -253,6 +336,7 @@
int index = i*l.n + n;
int p_index = index * (l.classes + 5) + 4;
float scale = predictions[p_index];
+ if(l.classfix == -1 && scale < .5) scale = 0;
int box_index = index * (l.classes + 5);
boxes[index] = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);
boxes[index].x *= w;
@@ -262,19 +346,26 @@
int class_index = index * (l.classes + 5) + 5;
if(l.softmax_tree){
-
+
hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);
int found = 0;
- for(j = l.classes - 1; j >= 0; --j){
- if(!found && predictions[class_index + j] > .5){
- found = 1;
- } else {
- predictions[class_index + j] = 0;
+ if(map){
+ for(j = 0; j < 200; ++j){
+ float prob = scale*predictions[class_index+map[j]];
+ probs[index][j] = (prob > thresh) ? prob : 0;
}
- float prob = predictions[class_index+j];
- probs[index][j] = (scale > thresh) ? prob : 0;
+ } else {
+ for(j = l.classes - 1; j >= 0; --j){
+ if(!found && predictions[class_index + j] > .5){
+ found = 1;
+ } else {
+ predictions[class_index + j] = 0;
+ }
+ float prob = predictions[class_index+j];
+ probs[index][j] = (scale > thresh) ? prob : 0;
+ }
}
- }else{
+ } else {
for(j = 0; j < l.classes; ++j){
float prob = scale*predictions[class_index+j];
probs[index][j] = (prob > thresh) ? prob : 0;
@@ -297,6 +388,18 @@
return;
}
*/
+ flatten_ongpu(state.input, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 1, l.output_gpu);
+ if(l.softmax_tree){
+ int i;
+ int count = 5;
+ for (i = 0; i < l.softmax_tree->groups; ++i) {
+ int group_size = l.softmax_tree->group_size[i];
+ softmax_gpu(l.output_gpu+count, group_size, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + count);
+ count += group_size;
+ }
+ }else if (l.softmax){
+ softmax_gpu(l.output_gpu+5, l.classes, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + 5);
+ }
float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
float *truth_cpu = 0;
@@ -305,22 +408,22 @@
truth_cpu = calloc(num_truth, sizeof(float));
cuda_pull_array(state.truth, truth_cpu, num_truth);
}
- cuda_pull_array(state.input, in_cpu, l.batch*l.inputs);
+ cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs);
network_state cpu_state = state;
cpu_state.train = state.train;
cpu_state.truth = truth_cpu;
cpu_state.input = in_cpu;
forward_region_layer(l, cpu_state);
- cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
- cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
+ //cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
free(cpu_state.input);
+ if(!state.train) return;
+ cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
if(cpu_state.truth) free(cpu_state.truth);
}
void backward_region_layer_gpu(region_layer l, network_state state)
{
- axpy_ongpu(l.batch*l.outputs, 1, l.delta_gpu, 1, state.delta, 1);
- //copy_ongpu(l.batch*l.inputs, l.delta_gpu, 1, state.delta, 1);
+ flatten_ongpu(l.delta_gpu, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 0, state.delta);
}
#endif
--
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