From 989ab8c38a02fa7ea9c25108151736c62e81c972 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 24 Apr 2015 17:27:50 +0000
Subject: [PATCH] IOU loss function
---
src/detection_layer.c | 421 ++++++++++++++++++++++++++++++++++++++++++++++------
1 files changed, 373 insertions(+), 48 deletions(-)
diff --git a/src/detection_layer.c b/src/detection_layer.c
index 6537079..7eaabb4 100644
--- a/src/detection_layer.c
+++ b/src/detection_layer.c
@@ -1,72 +1,397 @@
-int detection_out_height(detection_layer layer)
+#include "detection_layer.h"
+#include "activations.h"
+#include "softmax_layer.h"
+#include "blas.h"
+#include "cuda.h"
+#include "utils.h"
+#include <stdio.h>
+#include <string.h>
+#include <stdlib.h>
+
+int get_detection_layer_locations(detection_layer layer)
{
- return layer.size + layer.h*layer.stride;
+ return layer.inputs / (layer.classes+layer.coords+layer.rescore+layer.background);
}
-int detection_out_width(detection_layer layer)
+int get_detection_layer_output_size(detection_layer layer)
{
- return layer.size + layer.w*layer.stride;
+ return get_detection_layer_locations(layer)*(layer.background + layer.classes + layer.coords);
}
-detection_layer *make_detection_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
+detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background, int nuisance)
{
- int i;
- size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
detection_layer *layer = calloc(1, sizeof(detection_layer));
- layer->h = h;
- layer->w = w;
- layer->c = c;
- layer->n = n;
+
layer->batch = batch;
- layer->stride = stride;
- layer->size = size;
- assert(c%n == 0);
+ layer->inputs = inputs;
+ layer->classes = classes;
+ layer->coords = coords;
+ layer->rescore = rescore;
+ layer->nuisance = nuisance;
+ layer->cost = calloc(1, sizeof(float));
+ layer->does_cost=1;
+ layer->background = background;
+ int outputs = get_detection_layer_output_size(*layer);
+ layer->output = calloc(batch*outputs, sizeof(float));
+ layer->delta = calloc(batch*outputs, sizeof(float));
+ #ifdef GPU
+ layer->output_gpu = cuda_make_array(0, batch*outputs);
+ layer->delta_gpu = cuda_make_array(0, batch*outputs);
+ #endif
- layer->filters = calloc(c*size*size, sizeof(float));
- layer->filter_updates = calloc(c*size*size, sizeof(float));
- layer->filter_momentum = calloc(c*size*size, sizeof(float));
-
- float scale = 1./(size*size*c);
- for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*(rand_uniform());
-
- int out_h = detection_out_height(*layer);
- int out_w = detection_out_width(*layer);
-
- layer->output = calloc(layer->batch * out_h * out_w * n, sizeof(float));
- layer->delta = calloc(layer->batch * out_h * out_w * n, sizeof(float));
-
- layer->activation = activation;
-
- fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
+ fprintf(stderr, "Detection Layer\n");
srand(0);
return layer;
}
-void forward_detection_layer(const detection_layer layer, float *in)
+void dark_zone(detection_layer layer, int class, int start, network_state state)
{
- int out_h = detection_out_height(layer);
- int out_w = detection_out_width(layer);
- int i,j,fh, fw,c;
- memset(layer.output, 0, layer->batch*layer->n*out_h*out_w*sizeof(float));
- for(c = 0; c < layer.c; ++c){
- for(i = 0; i < layer.h; ++i){
- for(j = 0; j < layer.w; ++j){
- float val = layer->input[j+(i + c*layer.h)*layer.w];
- for(fh = 0; fh < layer.size; ++fh){
- for(fw = 0; fw < layer.size; ++fw){
- int h = i*layer.stride + fh;
- int w = j*layer.stride + fw;
- layer.output[w+(h+c/n*out_h)*out_w] += val*layer->filters[fw+(fh+c*layer.size)*layer.size];
- }
- }
- }
+ int index = start+layer.background+class;
+ int size = layer.classes+layer.coords+layer.background;
+ int location = (index%(7*7*size)) / size ;
+ int r = location / 7;
+ int c = location % 7;
+ int dr, dc;
+ for(dr = -1; dr <= 1; ++dr){
+ for(dc = -1; dc <= 1; ++dc){
+ if(!(dr || dc)) continue;
+ if((r + dr) > 6 || (r + dr) < 0) continue;
+ if((c + dc) > 6 || (c + dc) < 0) continue;
+ int di = (dr*7 + dc) * size;
+ if(state.truth[index+di]) continue;
+ layer.output[index + di] = 0;
+ //if(!state.truth[start+di]) continue;
+ //layer.output[start + di] = 1;
}
}
}
-void backward_detection_layer(const detection_layer layer, float *delta)
+typedef struct{
+ float dx, dy, dw, dh;
+} dbox;
+
+dbox derivative(box a, box b)
{
+ dbox d;
+ d.dx = 0;
+ d.dw = 0;
+ float l1 = a.x - a.w/2;
+ float l2 = b.x - b.w/2;
+ if (l1 > l2){
+ d.dx -= 1;
+ d.dw += .5;
+ }
+ float r1 = a.x + a.w/2;
+ float r2 = b.x + b.w/2;
+ if(r1 < r2){
+ d.dx += 1;
+ d.dw += .5;
+ }
+ if (l1 > r2) {
+ d.dx = -1;
+ d.dw = 0;
+ }
+ if (r1 < l2){
+ d.dx = 1;
+ d.dw = 0;
+ }
+
+ d.dy = 0;
+ d.dh = 0;
+ float t1 = a.y - a.h/2;
+ float t2 = b.y - b.h/2;
+ if (t1 > t2){
+ d.dy -= 1;
+ d.dh += .5;
+ }
+ float b1 = a.y + a.h/2;
+ float b2 = b.y + b.h/2;
+ if(b1 < b2){
+ d.dy += 1;
+ d.dh += .5;
+ }
+ if (t1 > b2) {
+ d.dy = -1;
+ d.dh = 0;
+ }
+ if (b1 < t2){
+ d.dy = 1;
+ d.dh = 0;
+ }
+ return d;
}
+float overlap(float x1, float w1, float x2, float w2)
+{
+ float l1 = x1 - w1/2;
+ float l2 = x2 - w2/2;
+ float left = l1 > l2 ? l1 : l2;
+ float r1 = x1 + w1/2;
+ float r2 = x2 + w2/2;
+ float right = r1 < r2 ? r1 : r2;
+ return right - left;
+}
+
+float box_intersection(box a, box b)
+{
+ float w = overlap(a.x, a.w, b.x, b.w);
+ float h = overlap(a.y, a.h, b.y, b.h);
+ if(w < 0 || h < 0) return 0;
+ float area = w*h;
+ return area;
+}
+
+float box_union(box a, box b)
+{
+ float i = box_intersection(a, b);
+ float u = a.w*a.h + b.w*b.h - i;
+ return u;
+}
+
+float box_iou(box a, box b)
+{
+ return box_intersection(a, b)/box_union(a, b);
+}
+
+dbox dintersect(box a, box b)
+{
+ float w = overlap(a.x, a.w, b.x, b.w);
+ float h = overlap(a.y, a.h, b.y, b.h);
+ dbox dover = derivative(a, b);
+ dbox di;
+
+ di.dw = dover.dw*h;
+ di.dx = dover.dx*h;
+ di.dh = dover.dh*w;
+ di.dy = dover.dy*w;
+ if(h < 0 || w < 0){
+ di.dx = dover.dx;
+ di.dy = dover.dy;
+ }
+ return di;
+}
+
+dbox dunion(box a, box b)
+{
+ dbox du = {0,0,0,0};;
+ float w = overlap(a.x, a.w, b.x, b.w);
+ float h = overlap(a.y, a.h, b.y, b.h);
+ if(w > 0 && h > 0){
+ dbox di = dintersect(a, b);
+ du.dw = h - di.dw;
+ du.dh = w - di.dw;
+ du.dx = -di.dx;
+ du.dy = -di.dy;
+ }
+ return du;
+}
+
+dbox diou(box a, box b)
+{
+ float u = box_union(a,b);
+ float i = box_intersection(a,b);
+ dbox di = dintersect(a,b);
+ dbox du = dunion(a,b);
+ dbox dd = {0,0,0,0};
+ if(i < 0) {
+ dd.dx = b.x - a.x;
+ dd.dy = b.y - a.y;
+ dd.dw = b.w - a.w;
+ dd.dh = b.h - a.h;
+ return dd;
+ }
+ dd.dx = 2*pow((1-(i/u)),1)*(di.dx*u - du.dx*i)/(u*u);
+ dd.dy = 2*pow((1-(i/u)),1)*(di.dy*u - du.dy*i)/(u*u);
+ dd.dw = 2*pow((1-(i/u)),1)*(di.dw*u - du.dw*i)/(u*u);
+ dd.dh = 2*pow((1-(i/u)),1)*(di.dh*u - du.dh*i)/(u*u);
+ return dd;
+}
+
+void test_box()
+{
+ box a = {1, 1, 1, 1};
+ box b = {0, 0, .5, .2};
+ int count = 0;
+ while(count++ < 300){
+ dbox d = diou(a, b);
+ printf("%f %f %f %f\n", a.x, a.y, a.w, a.h);
+ a.x += .1*d.dx;
+ a.w += .1*d.dw;
+ a.y += .1*d.dy;
+ a.h += .1*d.dh;
+ printf("inter: %f\n", box_intersection(a, b));
+ printf("union: %f\n", box_union(a, b));
+ printf("IOU: %f\n", box_iou(a, b));
+ if(d.dx==0 && d.dw==0 && d.dy==0 && d.dh==0) {
+ printf("break!!!\n");
+ break;
+ }
+ }
+}
+
+void forward_detection_layer(const detection_layer layer, network_state state)
+{
+ int in_i = 0;
+ int out_i = 0;
+ int locations = get_detection_layer_locations(layer);
+ int i,j;
+ for(i = 0; i < layer.batch*locations; ++i){
+ int mask = (!state.truth || state.truth[out_i + layer.background + layer.classes + 2]);
+ float scale = 1;
+ if(layer.rescore) 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.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);
+ }
+ for(j = 0; j < layer.coords; ++j){
+ layer.output[out_i++] = mask*state.input[in_i++];
+ }
+ }
+ if(layer.does_cost){
+ *(layer.cost) = 0;
+ int size = get_detection_layer_output_size(layer) * layer.batch;
+ memset(layer.delta, 0, size * sizeof(float));
+ for(i = 0; i < layer.batch*locations; ++i){
+ int classes = layer.nuisance+layer.classes;
+ int offset = i*(classes+layer.coords);
+ for(j = offset; j < offset+classes; ++j){
+ *(layer.cost) += pow(state.truth[j] - layer.output[j], 2);
+ layer.delta[j] = state.truth[j] - layer.output[j];
+ }
+ box truth;
+ truth.x = state.truth[j+0];
+ truth.y = state.truth[j+1];
+ truth.w = state.truth[j+2];
+ truth.h = state.truth[j+3];
+ box out;
+ out.x = layer.output[j+0];
+ out.y = layer.output[j+1];
+ out.w = layer.output[j+2];
+ out.h = layer.output[j+3];
+ if(!(truth.w*truth.h)) continue;
+ float iou = box_iou(truth, out);
+ //printf("iou: %f\n", iou);
+ *(layer.cost) += pow((1-iou), 2);
+ dbox d = diou(out, truth);
+ layer.delta[j+0] = d.dx;
+ layer.delta[j+1] = d.dy;
+ layer.delta[j+2] = d.dw;
+ layer.delta[j+3] = d.dh;
+ }
+ }
+ /*
+ int count = 0;
+ for(i = 0; i < layer.batch*locations; ++i){
+ for(j = 0; j < layer.classes+layer.background; ++j){
+ printf("%f, ", layer.output[count++]);
+ }
+ printf("\n");
+ for(j = 0; j < layer.coords; ++j){
+ printf("%f, ", layer.output[count++]);
+ }
+ printf("\n");
+ }
+ */
+ /*
+ if(layer.background || 1){
+ for(i = 0; i < layer.batch*locations; ++i){
+ int index = i*(layer.classes+layer.coords+layer.background);
+ for(j= 0; j < layer.classes; ++j){
+ if(state.truth[index+j+layer.background]){
+//dark_zone(layer, j, index, state);
+}
+}
+}
+}
+ */
+}
+
+void backward_detection_layer(const detection_layer layer, network_state state)
+{
+ int locations = get_detection_layer_locations(layer);
+ int i,j;
+ int in_i = 0;
+ int out_i = 0;
+ for(i = 0; i < layer.batch*locations; ++i){
+ float scale = 1;
+ float latent_delta = 0;
+ if(layer.rescore) scale = state.input[in_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.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++];
+ }
+ if(layer.rescore) state.delta[in_i-layer.coords-layer.classes-layer.rescore-layer.background] = latent_delta;
+ }
+}
+
+#ifdef GPU
+
+void forward_detection_layer_gpu(const detection_layer layer, network_state state)
+{
+ int outputs = get_detection_layer_output_size(layer);
+ float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
+ float *truth_cpu = 0;
+ if(state.truth){
+ truth_cpu = calloc(layer.batch*outputs, sizeof(float));
+ cuda_pull_array(state.truth, truth_cpu, layer.batch*outputs);
+ }
+ cuda_pull_array(state.input, in_cpu, layer.batch*layer.inputs);
+ network_state cpu_state;
+ cpu_state.train = state.train;
+ cpu_state.truth = truth_cpu;
+ cpu_state.input = in_cpu;
+ forward_detection_layer(layer, cpu_state);
+ cuda_push_array(layer.output_gpu, layer.output, layer.batch*outputs);
+ cuda_push_array(layer.delta_gpu, layer.delta, layer.batch*outputs);
+ free(cpu_state.input);
+ if(cpu_state.truth) free(cpu_state.truth);
+}
+
+void backward_detection_layer_gpu(detection_layer layer, network_state state)
+{
+ int outputs = get_detection_layer_output_size(layer);
+
+ float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
+ float *delta_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
+ float *truth_cpu = 0;
+ if(state.truth){
+ truth_cpu = calloc(layer.batch*outputs, sizeof(float));
+ cuda_pull_array(state.truth, truth_cpu, layer.batch*outputs);
+ }
+ network_state cpu_state;
+ cpu_state.train = state.train;
+ cpu_state.input = in_cpu;
+ cpu_state.truth = truth_cpu;
+ cpu_state.delta = delta_cpu;
+
+ cuda_pull_array(state.input, in_cpu, layer.batch*layer.inputs);
+ cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*outputs);
+ backward_detection_layer(layer, cpu_state);
+ cuda_push_array(state.delta, delta_cpu, layer.batch*layer.inputs);
+
+ free(in_cpu);
+ free(delta_cpu);
+}
+#endif
--
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