From 028696bf15efeca3acb3db8c42a96f7b9e0f55ff Mon Sep 17 00:00:00 2001
From: iovodov <b@ovdv.ru>
Date: Thu, 03 May 2018 13:33:46 +0000
Subject: [PATCH] Output improvements for detector results: When printing detector results, output was done in random order, obfuscating results for interpreting. Now: 1. Text output includes coordinates of rects in (left,right,top,bottom in pixels) along with label and score 2. Text output is sorted by rect lefts to simplify finding appropriate rects on image 3. If several class probs are > thresh for some detection, the most probable is written first and coordinates for others are not repeated 4. Rects are imprinted in image in order by their best class prob, so most probable rects are always on top and not overlayed by less probable ones 5. Most probable label for rect is always written first Also: 6. Message about low GPU memory include required amount
---
src/region_layer.c | 630 ++++++++++++++++++++++++++++++++++++++++++++++-----------
1 files changed, 507 insertions(+), 123 deletions(-)
diff --git a/src/region_layer.c b/src/region_layer.c
index dcdcfad..62c8b34 100644
--- a/src/region_layer.c
+++ b/src/region_layer.c
@@ -1,6 +1,5 @@
#include "region_layer.h"
#include "activations.h"
-#include "softmax_layer.h"
#include "blas.h"
#include "box.h"
#include "cuda.h"
@@ -10,180 +9,565 @@
#include <string.h>
#include <stdlib.h>
-region_layer make_region_layer(int batch, int inputs, int n, int side, int classes, int coords, int rescore)
+#define DOABS 1
+
+region_layer make_region_layer(int batch, int w, int h, int n, int classes, int coords, int max_boxes)
{
region_layer l = {0};
l.type = REGION;
-
+
l.n = n;
l.batch = batch;
- l.inputs = inputs;
+ l.h = h;
+ l.w = w;
l.classes = classes;
l.coords = coords;
- l.rescore = rescore;
- l.side = side;
- assert(side*side*l.coords*l.n == inputs);
l.cost = calloc(1, sizeof(float));
- int outputs = l.n*5*side*side;
- l.outputs = outputs;
- l.output = calloc(batch*outputs, sizeof(float));
- l.delta = calloc(batch*inputs, sizeof(float));
- #ifdef GPU
- l.output_gpu = cuda_make_array(l.output, batch*outputs);
- l.delta_gpu = cuda_make_array(l.delta, batch*inputs);
+ l.biases = calloc(n*2, sizeof(float));
+ l.bias_updates = calloc(n*2, sizeof(float));
+ l.outputs = h*w*n*(classes + coords + 1);
+ l.inputs = l.outputs;
+ l.max_boxes = max_boxes;
+ l.truths = max_boxes*(5);
+ l.delta = calloc(batch*l.outputs, sizeof(float));
+ l.output = calloc(batch*l.outputs, sizeof(float));
+ int i;
+ for(i = 0; i < n*2; ++i){
+ l.biases[i] = .5;
+ }
+
+ l.forward = forward_region_layer;
+ l.backward = backward_region_layer;
+#ifdef GPU
+ l.forward_gpu = forward_region_layer_gpu;
+ l.backward_gpu = backward_region_layer_gpu;
+ l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
+ 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;
}
+void resize_region_layer(layer *l, int w, int h)
+{
+ int old_w = l->w;
+ int old_h = l->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
+ if (old_w < w || old_h < h) {
+ 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 + 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;
+}
+
+float delta_region_box(box truth, float *x, float *biases, int n, int index, int i, int j, int w, int h, float *delta, float scale)
+{
+ box pred = get_region_box(x, biases, n, index, i, j, w, h);
+ float iou = box_iou(pred, truth);
+
+ 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]);
+ 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_id, int classes, tree *hier, float scale, float *avg_cat, int focal_loss)
+{
+ int i, n;
+ if(hier){
+ float pred = 1;
+ while(class_id >= 0){
+ pred *= output[index + class_id];
+ int g = hier->group[class_id];
+ 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_id] = scale * (1 - output[index + class_id]);
+
+ class_id = hier->parent[class_id];
+ }
+ *avg_cat += pred;
+ } else {
+ // Focal loss
+ if (focal_loss) {
+ // Focal Loss
+ float alpha = 0.5; // 0.25 or 0.5
+ //float gamma = 2; // hardcoded in many places of the grad-formula
+
+ int ti = index + class_id;
+ float pt = output[ti] + 0.000000000000001F;
+ // http://fooplot.com/#W3sidHlwZSI6MCwiZXEiOiItKDEteCkqKDIqeCpsb2coeCkreC0xKSIsImNvbG9yIjoiIzAwMDAwMCJ9LHsidHlwZSI6MTAwMH1d
+ float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832
+ //float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss
+
+ for (n = 0; n < classes; ++n) {
+ delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]);
+
+ delta[index + n] *= alpha*grad;
+
+ if (n == class_id) *avg_cat += output[index + n];
+ }
+ }
+ else {
+ // default
+ for (n = 0; n < classes; ++n) {
+ delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]);
+ if (n == class_id) *avg_cat += output[index + n];
+ }
+ }
+ }
+}
+
+float logit(float x)
+{
+ return log(x/(1.-x));
+}
+
+float tisnan(float x)
+{
+ return (x != x);
+}
+
+static int entry_index(layer l, int batch, int location, int entry)
+{
+ int n = location / (l.w*l.h);
+ int loc = location % (l.w*l.h);
+ return batch*l.outputs + n*l.w*l.h*(l.coords + l.classes + 1) + entry*l.w*l.h + loc;
+}
+
+void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output);
void forward_region_layer(const region_layer l, network_state state)
{
- int locations = l.side*l.side;
- int i,j;
- for(i = 0; i < l.batch*locations; ++i){
- for(j = 0; j < l.n; ++j){
- int in_index = i*l.n*l.coords + j*l.coords;
- int out_index = i*l.n*5 + j*5;
-
- float prob = state.input[in_index+0];
- float x = state.input[in_index+1];
- float y = state.input[in_index+2];
- float w = state.input[in_index+3];
- float h = state.input[in_index+4];
- /*
- float min_w = state.input[in_index+5];
- float max_w = state.input[in_index+6];
- float min_h = state.input[in_index+7];
- float max_h = state.input[in_index+8];
- */
-
- l.output[out_index+0] = prob;
- l.output[out_index+1] = x;
- l.output[out_index+2] = y;
- l.output[out_index+3] = w;
- l.output[out_index+4] = h;
-
+ int i,j,b,t,n;
+ int size = l.coords + l.classes + 1;
+ memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
+ #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(state.train){
- float avg_iou = 0;
- int count = 0;
- *(l.cost) = 0;
- int size = l.inputs * l.batch;
- memset(l.delta, 0, size * sizeof(float));
- for (i = 0; i < l.batch*locations; ++i) {
- for(j = 0; j < l.n; ++j){
- int in_index = i*l.n*l.coords + j*l.coords;
- l.delta[in_index+0] = .1*(0-state.input[in_index+0]);
+
+#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);
}
-
- int truth_index = i*5;
- int best_index = -1;
- float best_iou = 0;
- float best_rmse = 4;
-
- int bg = !state.truth[truth_index];
- if(bg) continue;
-
- box truth = {state.truth[truth_index+1], state.truth[truth_index+2], state.truth[truth_index+3], state.truth[truth_index+4]};
- truth.x /= l.side;
- truth.y /= l.side;
-
- for(j = 0; j < l.n; ++j){
- int out_index = i*l.n*5 + j*5;
- box out = {l.output[out_index+1], l.output[out_index+2], l.output[out_index+3], l.output[out_index+4]};
-
- //printf("\n%f %f %f %f %f\n", l.output[out_index+0], out.x, out.y, out.w, out.h);
-
- out.x /= l.side;
- out.y /= l.side;
-
- float iou = box_iou(out, truth);
- float rmse = box_rmse(out, truth);
- if(best_iou > 0 || iou > 0){
- if(iou > best_iou){
- best_iou = iou;
- best_index = j;
+ }
+ } 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, 1);
+ }
+ }
+ }
+#endif
+ if(!state.train) return;
+ memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
+ float avg_iou = 0;
+ float recall = 0;
+ float avg_cat = 0;
+ 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_id = 0;
+ for(t = 0; t < l.max_boxes; ++t){
+ box truth = float_to_box(state.truth + t*5 + b*l.truths);
+ if(!truth.x) break;
+ int class_id = 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_id);
+ if(p > maxp){
+ maxp = p;
+ maxi = n;
+ }
}
- }else{
- if(rmse < best_rmse){
- best_rmse = rmse;
- best_index = j;
+ int index = size*maxi + b*l.outputs + 5;
+ delta_region_class(l.output, l.delta, index, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss);
+ ++class_count;
+ onlyclass_id = 1;
+ break;
+ }
+ }
+ if(onlyclass_id) 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_id = -1;
+ for(t = 0; t < l.max_boxes; ++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_class_id = 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(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_id, l.classes, l.softmax_tree, l.class_scale*(l.classfix == 2 ? l.output[index + 4] : 1), &avg_cat, l.focal_loss);
+ ++class_count;
+ }
+ }
+ }
+
+ if(*(state.net.seen) < 12800){
+ box truth = {0};
+ truth.x = (i + .5)/l.w;
+ 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);
}
}
}
- printf("%d", best_index);
- //int out_index = i*l.n*5 + best_index*5;
- //box out = {l.output[out_index+1], l.output[out_index+2], l.output[out_index+3], l.output[out_index+4]};
- int in_index = i*l.n*l.coords + best_index*l.coords;
-
- l.delta[in_index+0] = (1-state.input[in_index+0]);
- l.delta[in_index+1] = state.truth[truth_index+1] - state.input[in_index+1];
- l.delta[in_index+2] = state.truth[truth_index+2] - state.input[in_index+2];
- l.delta[in_index+3] = state.truth[truth_index+3] - state.input[in_index+3];
- l.delta[in_index+4] = state.truth[truth_index+4] - state.input[in_index+4];
- /*
- l.delta[in_index+5] = 0 - state.input[in_index+5];
- l.delta[in_index+6] = 1 - state.input[in_index+6];
- l.delta[in_index+7] = 0 - state.input[in_index+7];
- l.delta[in_index+8] = 1 - state.input[in_index+8];
- */
-
- /*
- float x = state.input[in_index+1];
- float y = state.input[in_index+2];
- float w = state.input[in_index+3];
- float h = state.input[in_index+4];
- float min_w = state.input[in_index+5];
- float max_w = state.input[in_index+6];
- float min_h = state.input[in_index+7];
- float max_h = state.input[in_index+8];
- */
-
-
- avg_iou += best_iou;
- ++count;
}
- printf("\nAvg IOU: %f %d\n", avg_iou/count, count);
+ for(t = 0; t < l.max_boxes; ++t){
+ box truth = float_to_box(state.truth + t*5 + b*l.truths);
+
+ if(!truth.x) break;
+ float best_iou = 0;
+ int best_index = 0;
+ int best_n = 0;
+ i = (truth.x * l.w);
+ j = (truth.y * l.h);
+ //printf("%d %f %d %f\n", i, truth.x*l.w, j, truth.y*l.h);
+ box truth_shift = truth;
+ truth_shift.x = 0;
+ truth_shift.y = 0;
+ //printf("index %d %d\n",i, j);
+ 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);
+ 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;
+ pred.y = 0;
+ float iou = box_iou(pred, truth_shift);
+ if (iou > best_iou){
+ best_index = index;
+ best_iou = iou;
+ best_n = n;
+ }
+ }
+ //printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x, truth.y, truth.w, truth.h);
+
+ float iou = delta_region_box(truth, l.output, l.biases, best_n, best_index, i, j, l.w, l.h, l.delta, l.coord_scale);
+ if(iou > .5) recall += 1;
+ avg_iou += iou;
+
+ //l.delta[best_index + 4] = iou - l.output[best_index + 4];
+ avg_obj += l.output[best_index + 4];
+ l.delta[best_index + 4] = l.object_scale * (1 - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
+ if (l.rescore) {
+ l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
+ }
+
+
+ int class_id = state.truth[t*5 + b*l.truths + 4];
+ if (l.map) class_id = l.map[class_id];
+ delta_region_class(l.output, l.delta, best_index + 5, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss);
+ ++count;
+ ++class_count;
+ }
}
+ //printf("\n");
+ #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/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)
{
axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
- //copy_cpu(l.batch*l.inputs, 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, int *map)
+{
+ int i,j,n;
+ float *predictions = l.output;
+ for (i = 0; i < l.w*l.h; ++i){
+ int row = i / l.w;
+ int col = i % l.w;
+ for(n = 0; n < l.n; ++n){
+ 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;
+ boxes[index].y *= h;
+ boxes[index].w *= w;
+ boxes[index].h *= h;
+
+ 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;
+ if(map){
+ for(j = 0; j < 200; ++j){
+ float prob = scale*predictions[class_index+map[j]];
+ probs[index][j] = (prob > 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 {
+ for(j = 0; j < l.classes; ++j){
+ float prob = scale*predictions[class_index+j];
+ probs[index][j] = (prob > thresh) ? prob : 0;
+ }
+ }
+ if(only_objectness){
+ probs[index][0] = scale;
+ }
+ }
+ }
}
#ifdef GPU
void forward_region_layer_gpu(const region_layer l, network_state state)
{
+ /*
+ if(!state.train){
+ copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1);
+ 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;
if(state.truth){
- truth_cpu = calloc(l.batch*l.outputs, sizeof(float));
- cuda_pull_array(state.truth, truth_cpu, l.batch*l.outputs);
+ int num_truth = l.batch*l.truths;
+ 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);
- network_state cpu_state;
+ cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs);
+ //cudaStreamSynchronize(get_cuda_stream());
+ 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.inputs);
+ //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);
+ //cudaStreamSynchronize(get_cuda_stream());
if(cpu_state.truth) free(cpu_state.truth);
}
void backward_region_layer_gpu(region_layer l, network_state state)
{
- axpy_ongpu(l.batch*l.inputs, 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
+
+void correct_region_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative)
+{
+ int i;
+ int new_w = 0;
+ int new_h = 0;
+ if (((float)netw / w) < ((float)neth / h)) {
+ new_w = netw;
+ new_h = (h * netw) / w;
+ }
+ else {
+ new_h = neth;
+ new_w = (w * neth) / h;
+ }
+ for (i = 0; i < n; ++i) {
+ box b = dets[i].bbox;
+ b.x = (b.x - (netw - new_w) / 2. / netw) / ((float)new_w / netw);
+ b.y = (b.y - (neth - new_h) / 2. / neth) / ((float)new_h / neth);
+ b.w *= (float)netw / new_w;
+ b.h *= (float)neth / new_h;
+ if (!relative) {
+ b.x *= w;
+ b.w *= w;
+ b.y *= h;
+ b.h *= h;
+ }
+ dets[i].bbox = b;
+ }
+}
+
+
+void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets)
+{
+ int i, j, n, z;
+ float *predictions = l.output;
+ if (l.batch == 2) {
+ float *flip = l.output + l.outputs;
+ for (j = 0; j < l.h; ++j) {
+ for (i = 0; i < l.w / 2; ++i) {
+ for (n = 0; n < l.n; ++n) {
+ for (z = 0; z < l.classes + l.coords + 1; ++z) {
+ int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i;
+ int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1);
+ float swap = flip[i1];
+ flip[i1] = flip[i2];
+ flip[i2] = swap;
+ if (z == 0) {
+ flip[i1] = -flip[i1];
+ flip[i2] = -flip[i2];
+ }
+ }
+ }
+ }
+ }
+ for (i = 0; i < l.outputs; ++i) {
+ l.output[i] = (l.output[i] + flip[i]) / 2.;
+ }
+ }
+ for (i = 0; i < l.w*l.h; ++i) {
+ int row = i / l.w;
+ int col = i % l.w;
+ for (n = 0; n < l.n; ++n) {
+ int index = n*l.w*l.h + i;
+ for (j = 0; j < l.classes; ++j) {
+ dets[index].prob[j] = 0;
+ }
+ int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords);
+ int box_index = entry_index(l, 0, n*l.w*l.h + i, 0);
+ int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4);
+ float scale = l.background ? 1 : predictions[obj_index];
+ dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);// , l.w*l.h);
+ dets[index].objectness = scale > thresh ? scale : 0;
+ if (dets[index].mask) {
+ for (j = 0; j < l.coords - 4; ++j) {
+ dets[index].mask[j] = l.output[mask_index + j*l.w*l.h];
+ }
+ }
+
+ int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background);
+ if (l.softmax_tree) {
+
+ hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);// , l.w*l.h);
+ if (map) {
+ for (j = 0; j < 200; ++j) {
+ int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]);
+ float prob = scale*predictions[class_index];
+ dets[index].prob[j] = (prob > thresh) ? prob : 0;
+ }
+ }
+ else {
+ int j = hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh, l.w*l.h);
+ dets[index].prob[j] = (scale > thresh) ? scale : 0;
+ }
+ }
+ else {
+ if (dets[index].objectness) {
+ for (j = 0; j < l.classes; ++j) {
+ int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + j);
+ float prob = scale*predictions[class_index];
+ dets[index].prob[j] = (prob > thresh) ? prob : 0;
+ }
+ }
+ }
+ }
+ }
+ correct_region_boxes(dets, l.w*l.h*l.n, w, h, netw, neth, relative);
+}
--
Gitblit v1.10.0