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fix(yolov2): change static input size to dynamic

pull/3/head
Liu Yongkai 1 year ago
parent
af42ebca62
commit
545f1f1b3c
2 changed files with 87 additions and 129 deletions
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  1. 214
      APP_Framework/Framework/knowing/kpu-postprocessing/yolov2/region_layer.c
  2. 2
      APP_Framework/Framework/knowing/kpu-postprocessing/yolov2/region_layer.h

214
APP_Framework/Framework/knowing/kpu-postprocessing/yolov2/region_layer.c
View File

@ -1,66 +1,63 @@
#include <stdlib.h>
#include "region_layer.h"
#include <math.h>
#include <stdio.h>
#include "region_layer.h"
#include <stdlib.h>
typedef struct
{
typedef struct {
float x;
float y;
float w;
float h;
} box_t;
typedef struct
{
typedef struct {
int index;
int class;
float **probs;
} sortable_box_t;
int region_layer_init(region_layer_t *rl, int width, int height, int channels, int origin_width, int origin_height)
{
int flag = 0;
rl->coords = 4;
rl->image_width = 320;
rl->image_height = 240;
/* As no more parameter adding to this function,
image width(height) is regarded as net input shape as well as image capture from sensor.
If net input did not match sensor input, `dvp_set_image_size` function can set sensor output shape.
*/
rl->image_width = origin_width;
rl->image_height = origin_height;
rl->classes = channels / 5 - 5;
rl->net_width = origin_width;
rl->net_height = origin_height;
rl->layer_width = width;
rl->layer_height = height;
rl->boxes_number = (rl->layer_width * rl->layer_height * rl->anchor_number);
rl->boxes_number = (rl->layer_width * rl->layer_height * rl->anchor_number);
rl->output_number = (rl->boxes_number * (rl->classes + rl->coords + 1));
rl->output = malloc(rl->output_number * sizeof(float));
if (rl->output == NULL)
{
if (rl->output == NULL) {
flag = -1;
goto malloc_error;
}
rl->boxes = malloc(rl->boxes_number * sizeof(box_t));
if (rl->boxes == NULL)
{
if (rl->boxes == NULL) {
flag = -2;
goto malloc_error;
}
rl->probs_buf = malloc(rl->boxes_number * (rl->classes + 1) * sizeof(float));
if (rl->probs_buf == NULL)
{
if (rl->probs_buf == NULL) {
flag = -3;
goto malloc_error;
}
rl->probs = malloc(rl->boxes_number * sizeof(float *));
if (rl->probs == NULL)
{
if (rl->probs == NULL) {
flag = -4;
goto malloc_error;
}
for (uint32_t i = 0; i < rl->boxes_number; i++)
rl->probs[i] = &(rl->probs_buf[i * (rl->classes + 1)]);
for (uint32_t i = 0; i < rl->boxes_number; i++) rl->probs[i] = &(rl->probs_buf[i * (rl->classes + 1)]);
return 0;
malloc_error:
free(rl->output);
@ -78,24 +75,20 @@ void region_layer_deinit(region_layer_t *rl)
free(rl->probs);
}
static inline float sigmoid(float x)
{
return 1.f / (1.f + expf(-x));
}
static inline float sigmoid(float x) { return 1.f / (1.f + expf(-x)); }
static void activate_array(region_layer_t *rl, int index, int n)
{
float *output = &rl->output[index];
float *input = &rl->input[index];
for (int i = 0; i < n; ++i)
output[i] = sigmoid(input[i]);
for (int i = 0; i < n; ++i) output[i] = sigmoid(input[i]);
}
static int entry_index(region_layer_t *rl, int location, int entry)
{
int wh = rl->layer_width * rl->layer_height;
int n = location / wh;
int n = location / wh;
int loc = location % wh;
return n * wh * (rl->coords + rl->classes + 1) + entry * wh + loc;
@ -109,10 +102,8 @@ static void softmax(region_layer_t *rl, float *input, int n, int stride, float *
float sum = 0;
float largest_i = input[0];
for (i = 0; i < n; ++i)
{
if (input[i * stride] > largest_i)
largest_i = input[i * stride];
for (i = 0; i < n; ++i) {
if (input[i * stride] > largest_i) largest_i = input[i * stride];
}
for (i = 0; i < n; ++i) {
@ -121,17 +112,16 @@ static void softmax(region_layer_t *rl, float *input, int n, int stride, float *
sum += e;
output[i * stride] = e;
}
for (i = 0; i < n; ++i)
output[i * stride] /= sum;
for (i = 0; i < n; ++i) output[i * stride] /= sum;
}
static void softmax_cpu(region_layer_t *rl, float *input, int n, int batch, int batch_offset, int groups, int stride, float *output)
static void softmax_cpu(region_layer_t *rl, float *input, int n, int batch, int batch_offset, int groups, int stride,
float *output)
{
int g, b;
for (b = 0; b < batch; ++b) {
for (g = 0; g < groups; ++g)
softmax(rl, input + b * batch_offset + g, n, stride, output + b * batch_offset + g);
for (g = 0; g < groups; ++g) softmax(rl, input + b * batch_offset + g, n, stride, output + b * batch_offset + g);
}
}
@ -139,11 +129,9 @@ static void forward_region_layer(region_layer_t *rl)
{
int index;
for (index = 0; index < rl->output_number; index++)
rl->output[index] = rl->input[index];
for (index = 0; index < rl->output_number; index++) rl->output[index] = rl->input[index];
for (int n = 0; n < rl->anchor_number; ++n)
{
for (int n = 0; n < rl->anchor_number; ++n) {
index = entry_index(rl, n * rl->layer_width * rl->layer_height, 0);
activate_array(rl, index, 2 * rl->layer_width * rl->layer_height);
index = entry_index(rl, n * rl->layer_width * rl->layer_height, 4);
@ -151,9 +139,8 @@ static void forward_region_layer(region_layer_t *rl)
}
index = entry_index(rl, 0, rl->coords + 1);
softmax_cpu(rl, rl->input + index, rl->classes, rl->anchor_number,
rl->output_number / rl->anchor_number, rl->layer_width * rl->layer_height,
rl->layer_width * rl->layer_height, rl->output + index);
softmax_cpu(rl, rl->input + index, rl->classes, rl->anchor_number, rl->output_number / rl->anchor_number,
rl->layer_width * rl->layer_height, rl->layer_width * rl->layer_height, rl->output + index);
}
static void correct_region_boxes(region_layer_t *rl, box_t *boxes)
@ -166,8 +153,7 @@ static void correct_region_boxes(region_layer_t *rl, box_t *boxes)
int new_w = 0;
int new_h = 0;
if (((float)net_width / image_width) <
((float)net_height / image_height)) {
if (((float)net_width / image_width) < ((float)net_height / image_height)) {
new_w = net_width;
new_h = (image_height * net_width) / image_width;
} else {
@ -177,10 +163,8 @@ static void correct_region_boxes(region_layer_t *rl, box_t *boxes)
for (int i = 0; i < boxes_number; ++i) {
box_t b = boxes[i];
b.x = (b.x - (net_width - new_w) / 2. / net_width) /
((float)new_w / net_width);
b.y = (b.y - (net_height - new_h) / 2. / net_height) /
((float)new_h / net_height);
b.x = (b.x - (net_width - new_w) / 2. / net_width) / ((float)new_w / net_width);
b.y = (b.y - (net_height - new_h) / 2. / net_height) / ((float)new_h / net_height);
b.w *= (float)net_width / new_w;
b.h *= (float)net_height / new_h;
boxes[i] = b;
@ -207,34 +191,29 @@ static void get_region_boxes(region_layer_t *rl, float *predictions, float **pro
uint32_t coords = rl->coords;
float threshold = rl->threshold;
for (int i = 0; i < layer_width * layer_height; ++i)
{
for (int i = 0; i < layer_width * layer_height; ++i) {
int row = i / layer_width;
int col = i % layer_width;
for (int n = 0; n < anchor_number; ++n)
{
for (int n = 0; n < anchor_number; ++n) {
int index = n * layer_width * layer_height + i;
for (int j = 0; j < classes; ++j)
probs[index][j] = 0;
for (int j = 0; j < classes; ++j) probs[index][j] = 0;
int obj_index = entry_index(rl, n * layer_width * layer_height + i, coords);
int box_index = entry_index(rl, n * layer_width * layer_height + i, 0);
float scale = predictions[obj_index];
float scale = predictions[obj_index];
boxes[index] = get_region_box(predictions, rl->anchor, n, box_index, col, row,
layer_width, layer_height, layer_width * layer_height);
boxes[index] = get_region_box(predictions, rl->anchor, n, box_index, col, row, layer_width, layer_height,
layer_width * layer_height);
float max = 0;
for (int j = 0; j < classes; ++j)
{
for (int j = 0; j < classes; ++j) {
int class_index = entry_index(rl, n * layer_width * layer_height + i, coords + 1 + j);
float prob = scale * predictions[class_index];
probs[index][j] = (prob > threshold) ? prob : 0;
if (prob > max)
max = prob;
if (prob > max) max = prob;
}
probs[index][classes] = max;
}
@ -257,11 +236,11 @@ static int nms_comparator(void *pa, void *pb)
static float overlap(float x1, float w1, float x2, float w2)
{
float l1 = x1 - w1/2;
float l2 = x2 - w2/2;
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 r1 = x1 + w1 / 2;
float r2 = x2 + w2 / 2;
float right = r1 < r2 ? r1 : r2;
return right - left;
@ -272,8 +251,7 @@ static float box_intersection(box_t a, box_t 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;
if (w < 0 || h < 0) return 0;
return w * h;
}
@ -285,10 +263,7 @@ static float box_union(box_t a, box_t b)
return u;
}
static float box_iou(box_t a, box_t b)
{
return box_intersection(a, b) / box_union(a, b);
}
static float box_iou(box_t a, box_t b) { return box_intersection(a, b) / box_union(a, b); }
static void do_nms_sort(region_layer_t *rl, box_t *boxes, float **probs)
{
@ -298,30 +273,23 @@ static void do_nms_sort(region_layer_t *rl, box_t *boxes, float **probs)
int i, j, k;
sortable_box_t s[boxes_number];
for (i = 0; i < boxes_number; ++i)
{
for (i = 0; i < boxes_number; ++i) {
s[i].index = i;
s[i].class = 0;
s[i].probs = probs;
}
for (k = 0; k < classes; ++k)
{
for (i = 0; i < boxes_number; ++i)
s[i].class = k;
for (k = 0; k < classes; ++k) {
for (i = 0; i < boxes_number; ++i) s[i].class = k;
qsort(s, boxes_number, sizeof(sortable_box_t), nms_comparator);
for (i = 0; i < boxes_number; ++i)
{
if (probs[s[i].index][k] == 0)
continue;
for (i = 0; i < boxes_number; ++i) {
if (probs[s[i].index][k] == 0) continue;
box_t a = boxes[s[i].index];
for (j = i + 1; j < boxes_number; ++j)
{
for (j = i + 1; j < boxes_number; ++j) {
box_t b = boxes[s[j].index];
if (box_iou(a, b) > nms_value)
probs[s[j].index][k] = 0;
if (box_iou(a, b) > nms_value) probs[s[j].index][k] = 0;
}
}
}
@ -332,11 +300,9 @@ static int max_index(float *a, int n)
int i, max_i = 0;
float max = a[0];
for (i = 1; i < n; ++i)
{
if (a[i] > max)
{
max = a[i];
for (i = 1; i < n; ++i) {
if (a[i] > max) {
max = a[i];
max_i = i;
}
}
@ -351,14 +317,12 @@ static void region_layer_output(region_layer_t *rl, obj_info_t *obj_info)
uint32_t boxes_number = rl->boxes_number;
float threshold = rl->threshold;
box_t *boxes = (box_t *)rl->boxes;
for (int i = 0; i < rl->boxes_number; ++i)
{
int class = max_index(rl->probs[i], rl->classes);
for (int i = 0; i < rl->boxes_number; ++i) {
int class = max_index(rl->probs[i], rl->classes);
float prob = rl->probs[i][class];
if (prob > threshold)
{
if (prob > threshold) {
box_t *b = boxes + i;
obj_info->obj[obj_number].x1 = b->x * image_width - (b->w * image_width / 2);
obj_info->obj[obj_number].y1 = b->y * image_height - (b->h * image_height / 2);
@ -380,7 +344,8 @@ void region_layer_run(region_layer_t *rl, obj_info_t *obj_info)
region_layer_output(rl, obj_info);
}
void draw_edge(uint32_t *gram, obj_info_t *obj_info, uint32_t index, uint16_t color)
void draw_edge(uint32_t *gram, obj_info_t *obj_info, uint32_t index, uint16_t color, uint16_t image_width,
uint16_t image_height)
{
uint32_t data = ((uint32_t)color << 16) | (uint32_t)color;
uint32_t *addr1, *addr2, *addr3, *addr4, x1, y1, x2, y2;
@ -390,48 +355,41 @@ void draw_edge(uint32_t *gram, obj_info_t *obj_info, uint32_t index, uint16_t co
x2 = obj_info->obj[index].x2;
y2 = obj_info->obj[index].y2;
if (x1 <= 0)
x1 = 1;
if (x2 >= 319)
x2 = 318;
if (y1 <= 0)
y1 = 1;
if (y2 >= 239)
y2 = 238;
addr1 = gram + (320 * y1 + x1) / 2;
addr2 = gram + (320 * y1 + x2 - 8) / 2;
addr3 = gram + (320 * (y2 - 1) + x1) / 2;
addr4 = gram + (320 * (y2 - 1) + x2 - 8) / 2;
for (uint32_t i = 0; i < 4; i++)
{
if (x1 <= 0) x1 = 1;
if (x2 >= image_width - 1) x2 = image_width - 2;
if (y1 <= 0) y1 = 1;
if (y2 >= image_height - 1) y2 = image_height - 2;
addr1 = gram + (image_width * y1 + x1) / 2;
addr2 = gram + (image_width * y1 + x2 - 8) / 2;
addr3 = gram + (image_width * (y2 - 1) + x1) / 2;
addr4 = gram + (image_width * (y2 - 1) + x2 - 8) / 2;
for (uint32_t i = 0; i < 4; i++) {
*addr1 = data;
*(addr1 + 160) = data;
*(addr1 + image_width / 2) = data;
*addr2 = data;
*(addr2 + 160) = data;
*(addr2 + image_width / 2) = data;
*addr3 = data;
*(addr3 + 160) = data;
*(addr3 + image_width / 2) = data;
*addr4 = data;
*(addr4 + 160) = data;
*(addr4 + image_width / 2) = data;
addr1++;
addr2++;
addr3++;
addr4++;
}
addr1 = gram + (320 * y1 + x1) / 2;
addr2 = gram + (320 * y1 + x2 - 2) / 2;
addr3 = gram + (320 * (y2 - 8) + x1) / 2;
addr4 = gram + (320 * (y2 - 8) + x2 - 2) / 2;
for (uint32_t i = 0; i < 8; i++)
{
addr1 = gram + (image_width * y1 + x1) / 2;
addr2 = gram + (image_width * y1 + x2 - 2) / 2;
addr3 = gram + (image_width * (y2 - 8) + x1) / 2;
addr4 = gram + (image_width * (y2 - 8) + x2 - 2) / 2;
for (uint32_t i = 0; i < 8; i++) {
*addr1 = data;
*addr2 = data;
*addr3 = data;
*addr4 = data;
addr1 += 160;
addr2 += 160;
addr3 += 160;
addr4 += 160;
addr1 += image_width / 2;
addr2 += image_width / 2;
addr3 += image_width / 2;
addr4 += image_width / 2;
}
}

2
APP_Framework/Framework/knowing/kpu-postprocessing/yolov2/region_layer.h
View File

@ -44,6 +44,6 @@ typedef struct
int region_layer_init(region_layer_t *rl, int width, int height, int channels, int origin_width, int origin_height);
void region_layer_deinit(region_layer_t *rl);
void region_layer_run(region_layer_t *rl, obj_info_t *obj_info);
void draw_edge(uint32_t *gram, obj_info_t *obj_info, uint32_t index, uint16_t color);
void draw_edge(uint32_t *gram, obj_info_t *obj_info, uint32_t index, uint16_t color, uint16_t image_width, uint16_t image_height);
#endif // _REGION_LAYER

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