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APP_Framework/Framework/:add kpu postprocessing (yolov2) in knowing file

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chunyexixiaoyu 1 year ago
parent
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1c16e3a463
  1. 3
      APP_Framework/Framework/knowing/Kconfig
  2. 5
      APP_Framework/Framework/knowing/kpu-postprocessing/Kconfig
  3. 14
      APP_Framework/Framework/knowing/kpu-postprocessing/SConscript
  4. 7
      APP_Framework/Framework/knowing/kpu-postprocessing/yolov2/Kconfig
  5. 10
      APP_Framework/Framework/knowing/kpu-postprocessing/yolov2/SConscript
  6. 437
      APP_Framework/Framework/knowing/kpu-postprocessing/yolov2/region_layer.c
  7. 49
      APP_Framework/Framework/knowing/kpu-postprocessing/yolov2/region_layer.h

3
APP_Framework/Framework/knowing/Kconfig

@ -3,5 +3,6 @@ menuconfig SUPPORT_KNOWING_FRAMEWORK
default y
if SUPPORT_KNOWING_FRAMEWORK
source "$APP_DIR/Framework/knowing/tensorflow-lite/Kconfig"
source "$APP_DIR/Framework/knowing/tensorflow-lite/Kconfig"
source "$APP_DIR/Framework/knowing/kpu-postprocessing/Kconfig"
endif

5
APP_Framework/Framework/knowing/kpu-postprocessing/Kconfig

@ -0,0 +1,5 @@
menuconfig USING_KPU_POSTPROCESSING
bool "kpu model postprocessing"
default y
source "$APP_DIR/Framework/knowing/kpu-postprocessing/yolov2/Kconfig"

14
APP_Framework/Framework/knowing/kpu-postprocessing/SConscript

@ -0,0 +1,14 @@
import os
Import('RTT_ROOT')
from building import *
cwd = GetCurrentDir()
objs = []
list = os.listdir(cwd)
for d in list:
path = os.path.join(cwd, d)
if os.path.isfile(os.path.join(path, 'SConscript')):
objs = objs + SConscript(os.path.join(path, 'SConscript'))
Return('objs')

7
APP_Framework/Framework/knowing/kpu-postprocessing/yolov2/Kconfig

@ -0,0 +1,7 @@
menuconfig USING_YOLOV2
bool "yolov2 region layer"
depends on USING_KPU_POSTPROCESSING
default n

10
APP_Framework/Framework/knowing/kpu-postprocessing/yolov2/SConscript

@ -0,0 +1,10 @@
from building import *
import os
cwd = GetCurrentDir()
src = Glob('*.c')
group = DefineGroup('yolov2', src, depend = ['USING_YOLOV2'], CPPPATH = [cwd])
Return('group')

437
APP_Framework/Framework/knowing/kpu-postprocessing/yolov2/region_layer.c

@ -0,0 +1,437 @@
#include <stdlib.h>
#include <math.h>
#include <stdio.h>
#include "region_layer.h"
typedef struct
{
float x;
float y;
float w;
float h;
} box_t;
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;
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->output_number = (rl->boxes_number * (rl->classes + rl->coords + 1));
rl->output = malloc(rl->output_number * sizeof(float));
if (rl->output == NULL)
{
flag = -1;
goto malloc_error;
}
rl->boxes = malloc(rl->boxes_number * sizeof(box_t));
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)
{
flag = -3;
goto malloc_error;
}
rl->probs = malloc(rl->boxes_number * sizeof(float *));
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)]);
return 0;
malloc_error:
free(rl->output);
free(rl->boxes);
free(rl->probs_buf);
free(rl->probs);
return flag;
}
void region_layer_deinit(region_layer_t *rl)
{
free(rl->output);
free(rl->boxes);
free(rl->probs_buf);
free(rl->probs);
}
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]);
}
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 loc = location % wh;
return n * wh * (rl->coords + rl->classes + 1) + entry * wh + loc;
}
static void softmax(region_layer_t *rl, float *input, int n, int stride, float *output)
{
int i;
float diff;
float e;
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) {
diff = input[i * stride] - largest_i;
e = expf(diff);
sum += e;
output[i * stride] = e;
}
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)
{
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);
}
}
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 (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);
activate_array(rl, index, rl->layer_width * rl->layer_height);
}
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);
}
static void correct_region_boxes(region_layer_t *rl, box_t *boxes)
{
uint32_t net_width = rl->net_width;
uint32_t net_height = rl->net_height;
uint32_t image_width = rl->image_width;
uint32_t image_height = rl->image_height;
uint32_t boxes_number = rl->boxes_number;
int new_w = 0;
int new_h = 0;
if (((float)net_width / image_width) <
((float)net_height / image_height)) {
new_w = net_width;
new_h = (image_height * net_width) / image_width;
} else {
new_h = net_height;
new_w = (image_width * net_height) / image_height;
}
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.w *= (float)net_width / new_w;
b.h *= (float)net_height / new_h;
boxes[i] = b;
}
}
static box_t get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h, int stride)
{
volatile box_t b;
b.x = (i + x[index + 0 * stride]) / w;
b.y = (j + x[index + 1 * stride]) / h;
b.w = expf(x[index + 2 * stride]) * biases[2 * n] / w;
b.h = expf(x[index + 3 * stride]) * biases[2 * n + 1] / h;
return b;
}
static void get_region_boxes(region_layer_t *rl, float *predictions, float **probs, box_t *boxes)
{
uint32_t layer_width = rl->layer_width;
uint32_t layer_height = rl->layer_height;
uint32_t anchor_number = rl->anchor_number;
uint32_t classes = rl->classes;
uint32_t coords = rl->coords;
float threshold = rl->threshold;
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)
{
int index = n * layer_width * layer_height + i;
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];
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)
{
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;
}
probs[index][classes] = max;
}
}
correct_region_boxes(rl, boxes);
}
static int nms_comparator(void *pa, void *pb)
{
sortable_box_t a = *(sortable_box_t *)pa;
sortable_box_t b = *(sortable_box_t *)pb;
float diff = a.probs[a.index][b.class] - b.probs[b.index][b.class];
if (diff < 0)
return 1;
else if (diff > 0)
return -1;
return 0;
}
static 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;
}
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;
return w * h;
}
static float box_union(box_t a, box_t b)
{
float i = box_intersection(a, b);
float u = a.w * a.h + b.w * b.h - i;
return u;
}
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)
{
uint32_t boxes_number = rl->boxes_number;
uint32_t classes = rl->classes;
float nms_value = rl->nms_value;
int i, j, k;
sortable_box_t s[boxes_number];
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;
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;
box_t a = boxes[s[i].index];
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;
}
}
}
}
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];
max_i = i;
}
}
return max_i;
}
static void region_layer_output(region_layer_t *rl, obj_info_t *obj_info)
{
uint32_t obj_number = 0;
uint32_t image_width = rl->image_width;
uint32_t image_height = rl->image_height;
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);
float prob = rl->probs[i][class];
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);
obj_info->obj[obj_number].x2 = b->x * image_width + (b->w * image_width / 2);
obj_info->obj[obj_number].y2 = b->y * image_height + (b->h * image_height / 2);
obj_info->obj[obj_number].class_id = class;
obj_info->obj[obj_number].prob = prob;
obj_number++;
}
}
obj_info->obj_number = obj_number;
}
void region_layer_run(region_layer_t *rl, obj_info_t *obj_info)
{
forward_region_layer(rl);
get_region_boxes(rl, rl->output, rl->probs, rl->boxes);
do_nms_sort(rl, rl->boxes, rl->probs);
region_layer_output(rl, obj_info);
}
void draw_edge(uint32_t *gram, obj_info_t *obj_info, uint32_t index, uint16_t color)
{
uint32_t data = ((uint32_t)color << 16) | (uint32_t)color;
uint32_t *addr1, *addr2, *addr3, *addr4, x1, y1, x2, y2;
x1 = obj_info->obj[index].x1;
y1 = obj_info->obj[index].y1;
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++)
{
*addr1 = data;
*(addr1 + 160) = data;
*addr2 = data;
*(addr2 + 160) = data;
*addr3 = data;
*(addr3 + 160) = data;
*addr4 = data;
*(addr4 + 160) = 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 = data;
*addr2 = data;
*addr3 = data;
*addr4 = data;
addr1 += 160;
addr2 += 160;
addr3 += 160;
addr4 += 160;
}
}

49
APP_Framework/Framework/knowing/kpu-postprocessing/yolov2/region_layer.h

@ -0,0 +1,49 @@
#ifndef _REGION_LAYER
#define _REGION_LAYER
#include <stdint.h>
#include "kpu.h"
typedef struct
{
uint32_t obj_number;
struct
{
uint32_t x1;
uint32_t y1;
uint32_t x2;
uint32_t y2;
uint32_t class_id;
float prob;
} obj[10];
} obj_info_t;
typedef struct
{
float threshold;
float nms_value;
uint32_t coords;
uint32_t anchor_number;
float *anchor;
uint32_t image_width;
uint32_t image_height;
uint32_t classes;
uint32_t net_width;
uint32_t net_height;
uint32_t layer_width;
uint32_t layer_height;
uint32_t boxes_number;
uint32_t output_number;
void *boxes;
float *input;
float *output;
float *probs_buf;
float **probs;
} region_layer_t;
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);
#endif // _REGION_LAYER
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