658 lines
23 KiB
C++
658 lines
23 KiB
C++
// Copyright (c) 2021 by Rockchip Electronics Co., Ltd. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "yolov5.h"
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#include <math.h>
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#include <stdint.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <sys/time.h>
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#include "rknn.h"
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#include <set>
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#include <vector>
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// #define LABEL_NALE_TXT_PATH "./model/coco_80_labels_list.txt"
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char *labels[OBJ_MAX_CLASS_NUM];
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int codes[OBJ_MAX_CLASS_NUM];
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int OBJ_CLASS_REAL_NUM = 0;
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int PROP_BOX_SIZE = 0;
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const int anchor[3][6] = {{10, 13, 16, 30, 33, 23},
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{30, 61, 62, 45, 59, 119},
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{116, 90, 156, 198, 373, 326}};
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inline static int clamp(float val, int min, int max) { return val > min ? (val < max ? val : max) : min; }
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static char *readLine(FILE *fp, char *buffer, int *len)
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{
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int ch;
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int i = 0;
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size_t buff_len = 0;
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buffer = (char *)malloc(buff_len + 1);
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if (!buffer)
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return NULL; // Out of memory
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while ((ch = fgetc(fp)) != '\n' && ch != EOF)
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{
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buff_len++;
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void *tmp = realloc(buffer, buff_len + 1);
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if (tmp == NULL)
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{
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free(buffer);
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return NULL; // Out of memory
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}
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buffer = (char *)tmp;
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buffer[i] = (char)ch;
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i++;
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}
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buffer[i] = '\0';
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*len = buff_len;
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// Detect end
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if (ch == EOF && (i == 0 || ferror(fp)))
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{
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free(buffer);
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return NULL;
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}
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return buffer;
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}
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static int readLines(const char *fileName, char *lines[], int max_line)
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{
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FILE *file = fopen(fileName, "r");
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char *s;
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int i = 0;
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int n = 0;
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if (file == NULL)
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{
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printf("Open %s fail!\n", fileName);
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return -1;
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}
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while ((s = readLine(file, s, &n)) != NULL)
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{
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printf("now is %s\n", s);
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lines[i++] = s;
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if (i >= max_line)
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break;
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}
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OBJ_CLASS_REAL_NUM = i;
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PROP_BOX_SIZE = (OBJ_CLASS_REAL_NUM + 5);
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fclose(file);
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return i;
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}
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static int loadLabelName(const char *locationFilename, char *label[])
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{
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printf("load lable %s\n", locationFilename);
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readLines(locationFilename, label, OBJ_MAX_CLASS_NUM);
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for (int i = 0; i < OBJ_CLASS_REAL_NUM; i++) {
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printf("%d => %s\n", i, coco_cls_to_name(i));
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}
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return 0;
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}
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static float CalculateOverlap(float xmin0, float ymin0, float xmax0, float ymax0, float xmin1, float ymin1, float xmax1,
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float ymax1)
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{
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float w = fmax(0.f, fmin(xmax0, xmax1) - fmax(xmin0, xmin1) + 1.0);
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float h = fmax(0.f, fmin(ymax0, ymax1) - fmax(ymin0, ymin1) + 1.0);
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float i = w * h;
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float u = (xmax0 - xmin0 + 1.0) * (ymax0 - ymin0 + 1.0) + (xmax1 - xmin1 + 1.0) * (ymax1 - ymin1 + 1.0) - i;
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return u <= 0.f ? 0.f : (i / u);
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}
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static int nms(int validCount, std::vector<float> &outputLocations, std::vector<int> classIds, std::vector<int> &order,
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int filterId, float threshold)
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{
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for (int i = 0; i < validCount; ++i)
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{
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int n = order[i];
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if (n == -1 || classIds[n] != filterId)
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{
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continue;
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}
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for (int j = i + 1; j < validCount; ++j)
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{
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int m = order[j];
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if (m == -1 || classIds[m] != filterId)
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{
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continue;
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}
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float xmin0 = outputLocations[n * 4 + 0];
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float ymin0 = outputLocations[n * 4 + 1];
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float xmax0 = outputLocations[n * 4 + 0] + outputLocations[n * 4 + 2];
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float ymax0 = outputLocations[n * 4 + 1] + outputLocations[n * 4 + 3];
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float xmin1 = outputLocations[m * 4 + 0];
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float ymin1 = outputLocations[m * 4 + 1];
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float xmax1 = outputLocations[m * 4 + 0] + outputLocations[m * 4 + 2];
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float ymax1 = outputLocations[m * 4 + 1] + outputLocations[m * 4 + 3];
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float iou = CalculateOverlap(xmin0, ymin0, xmax0, ymax0, xmin1, ymin1, xmax1, ymax1);
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if (iou > threshold)
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{
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order[j] = -1;
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}
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}
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}
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return 0;
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}
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static int quick_sort_indice_inverse(std::vector<float> &input, int left, int right, std::vector<int> &indices)
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{
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float key;
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int key_index;
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int low = left;
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int high = right;
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if (left < right)
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{
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key_index = indices[left];
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key = input[left];
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while (low < high)
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{
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while (low < high && input[high] <= key)
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{
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high--;
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}
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input[low] = input[high];
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indices[low] = indices[high];
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while (low < high && input[low] >= key)
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{
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low++;
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}
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input[high] = input[low];
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indices[high] = indices[low];
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}
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input[low] = key;
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indices[low] = key_index;
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quick_sort_indice_inverse(input, left, low - 1, indices);
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quick_sort_indice_inverse(input, low + 1, right, indices);
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}
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return low;
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}
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static float sigmoid(float x) { return 1.0 / (1.0 + expf(-x)); }
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static float unsigmoid(float y) { return -1.0 * logf((1.0 / y) - 1.0); }
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inline static int32_t __clip(float val, float min, float max)
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{
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float f = val <= min ? min : (val >= max ? max : val);
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return f;
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}
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static int8_t qnt_f32_to_affine(float f32, int32_t zp, float scale)
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{
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float dst_val = (f32 / scale) + zp;
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int8_t res = (int8_t)__clip(dst_val, -128, 127);
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return res;
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}
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static uint8_t qnt_f32_to_affine_u8(float f32, int32_t zp, float scale)
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{
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float dst_val = (f32 / scale) + zp;
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uint8_t res = (uint8_t)__clip(dst_val, 0, 255);
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return res;
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}
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static float deqnt_affine_to_f32(int8_t qnt, int32_t zp, float scale) { return ((float)qnt - (float)zp) * scale; }
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static float deqnt_affine_u8_to_f32(uint8_t qnt, int32_t zp, float scale) { return ((float)qnt - (float)zp) * scale; }
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static int process_u8(uint8_t *input, int *anchor, int grid_h, int grid_w, int height, int width, int stride,
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std::vector<float> &boxes, std::vector<float> &objProbs, std::vector<int> &classId, float threshold,
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int32_t zp, float scale)
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{
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int validCount = 0;
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int grid_len = grid_h * grid_w;
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uint8_t thres_u8 = qnt_f32_to_affine_u8(threshold, zp, scale);
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for (int a = 0; a < 3; a++)
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{
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for (int i = 0; i < grid_h; i++)
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{
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for (int j = 0; j < grid_w; j++)
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{
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uint8_t box_confidence = input[(PROP_BOX_SIZE * a + 4) * grid_len + i * grid_w + j];
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if (box_confidence >= thres_u8)
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{
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int offset = (PROP_BOX_SIZE * a) * grid_len + i * grid_w + j;
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uint8_t *in_ptr = input + offset;
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float box_x = (deqnt_affine_u8_to_f32(*in_ptr, zp, scale)) * 2.0 - 0.5;
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float box_y = (deqnt_affine_u8_to_f32(in_ptr[grid_len], zp, scale)) * 2.0 - 0.5;
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float box_w = (deqnt_affine_u8_to_f32(in_ptr[2 * grid_len], zp, scale)) * 2.0;
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float box_h = (deqnt_affine_u8_to_f32(in_ptr[3 * grid_len], zp, scale)) * 2.0;
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box_x = (box_x + j) * (float)stride;
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box_y = (box_y + i) * (float)stride;
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box_w = box_w * box_w * (float)anchor[a * 2];
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box_h = box_h * box_h * (float)anchor[a * 2 + 1];
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box_x -= (box_w / 2.0);
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box_y -= (box_h / 2.0);
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uint8_t maxClassProbs = in_ptr[5 * grid_len];
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int maxClassId = 0;
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for (int k = 1; k < OBJ_CLASS_REAL_NUM; ++k)
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{
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uint8_t prob = in_ptr[(5 + k) * grid_len];
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if (prob > maxClassProbs)
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{
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maxClassId = k;
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maxClassProbs = prob;
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}
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}
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float limit_score = (deqnt_affine_u8_to_f32(maxClassProbs, zp, scale)) * (deqnt_affine_u8_to_f32(box_confidence, zp, scale));
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if (limit_score >= threshold)
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{
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objProbs.push_back(limit_score);
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classId.push_back(maxClassId);
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validCount++;
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boxes.push_back(box_x);
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boxes.push_back(box_y);
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boxes.push_back(box_w);
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boxes.push_back(box_h);
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}
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}
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}
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}
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}
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return validCount;
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}
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static int process_i8(int8_t *input, int *anchor, int grid_h, int grid_w, int height, int width, int stride,
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std::vector<float> &boxes, std::vector<float> &objProbs, std::vector<int> &classId, float threshold,
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int32_t zp, float scale)
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{
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int validCount = 0;
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int grid_len = grid_h * grid_w;
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int8_t thres_i8 = qnt_f32_to_affine(threshold, zp, scale);
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for (int a = 0; a < 3; a++)
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{
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for (int i = 0; i < grid_h; i++)
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{
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for (int j = 0; j < grid_w; j++)
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{
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int8_t box_confidence = input[(PROP_BOX_SIZE * a + 4) * grid_len + i * grid_w + j];
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if (box_confidence >= thres_i8)
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{
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int offset = (PROP_BOX_SIZE * a) * grid_len + i * grid_w + j;
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int8_t *in_ptr = input + offset;
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float box_x = (deqnt_affine_to_f32(*in_ptr, zp, scale)) * 2.0 - 0.5;
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float box_y = (deqnt_affine_to_f32(in_ptr[grid_len], zp, scale)) * 2.0 - 0.5;
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float box_w = (deqnt_affine_to_f32(in_ptr[2 * grid_len], zp, scale)) * 2.0;
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float box_h = (deqnt_affine_to_f32(in_ptr[3 * grid_len], zp, scale)) * 2.0;
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box_x = (box_x + j) * (float)stride;
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box_y = (box_y + i) * (float)stride;
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box_w = box_w * box_w * (float)anchor[a * 2];
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box_h = box_h * box_h * (float)anchor[a * 2 + 1];
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box_x -= (box_w / 2.0);
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box_y -= (box_h / 2.0);
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int8_t maxClassProbs = in_ptr[5 * grid_len];
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int maxClassId = 0;
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for (int k = 1; k < OBJ_CLASS_REAL_NUM; ++k)
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{
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int8_t prob = in_ptr[(5 + k) * grid_len];
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if (prob > maxClassProbs)
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{
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maxClassId = k;
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maxClassProbs = prob;
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}
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}
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float limit_score = (deqnt_affine_to_f32(maxClassProbs, zp, scale)) * (deqnt_affine_to_f32(box_confidence, zp, scale));
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if (limit_score >= threshold)
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{
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objProbs.push_back(limit_score);
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classId.push_back(maxClassId);
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validCount++;
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boxes.push_back(box_x);
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boxes.push_back(box_y);
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boxes.push_back(box_w);
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boxes.push_back(box_h);
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}
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}
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}
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}
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}
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return validCount;
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}
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static int process_i8_rv1106(int8_t *input, int *anchor, int grid_h, int grid_w, int height, int width, int stride,
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std::vector<float> &boxes, std::vector<float> &boxScores, std::vector<int> &classId, float threshold,
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int32_t zp, float scale) {
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int validCount = 0;
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int8_t thres_i8 = qnt_f32_to_affine(threshold, zp, scale);
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int anchor_per_branch = 3;
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int align_c = PROP_BOX_SIZE * anchor_per_branch;
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for (int h = 0; h < grid_h; h++) {
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for (int w = 0; w < grid_w; w++) {
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for (int a = 0; a < anchor_per_branch; a++) {
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int hw_offset = h * grid_w * align_c + w * align_c + a * PROP_BOX_SIZE;
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int8_t *hw_ptr = input + hw_offset;
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int8_t box_confidence = hw_ptr[4];
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if (box_confidence >= thres_i8) {
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int8_t maxClassProbs = hw_ptr[5];
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int maxClassId = 0;
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for (int k = 1; k < OBJ_CLASS_REAL_NUM; ++k) {
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int8_t prob = hw_ptr[5 + k];
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if (prob > maxClassProbs) {
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maxClassId = k;
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maxClassProbs = prob;
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}
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}
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float box_conf_f32 = deqnt_affine_to_f32(box_confidence, zp, scale);
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float class_prob_f32 = deqnt_affine_to_f32(maxClassProbs, zp, scale);
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float limit_score = box_conf_f32 * class_prob_f32;
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if (limit_score > threshold) {
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float box_x, box_y, box_w, box_h;
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box_x = deqnt_affine_to_f32(hw_ptr[0], zp, scale) * 2.0 - 0.5;
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box_y = deqnt_affine_to_f32(hw_ptr[1], zp, scale) * 2.0 - 0.5;
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box_w = deqnt_affine_to_f32(hw_ptr[2], zp, scale) * 2.0;
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box_h = deqnt_affine_to_f32(hw_ptr[3], zp, scale) * 2.0;
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box_w = box_w * box_w;
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box_h = box_h * box_h;
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box_x = (box_x + w) * (float)stride;
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box_y = (box_y + h) * (float)stride;
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box_w *= (float)anchor[a * 2];
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box_h *= (float)anchor[a * 2 + 1];
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box_x -= (box_w / 2.0);
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box_y -= (box_h / 2.0);
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boxes.push_back(box_x);
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boxes.push_back(box_y);
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boxes.push_back(box_w);
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boxes.push_back(box_h);
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boxScores.push_back(limit_score);
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classId.push_back(maxClassId);
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validCount++;
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}
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}
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}
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}
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}
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return validCount;
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}
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static int process_fp32(float *input, int *anchor, int grid_h, int grid_w, int height, int width, int stride,
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std::vector<float> &boxes, std::vector<float> &objProbs, std::vector<int> &classId, float threshold)
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{
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int validCount = 0;
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int grid_len = grid_h * grid_w;
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for (int a = 0; a < 3; a++)
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{
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for (int i = 0; i < grid_h; i++)
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{
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for (int j = 0; j < grid_w; j++)
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{
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float box_confidence = input[(PROP_BOX_SIZE * a + 4) * grid_len + i * grid_w + j];
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if (box_confidence >= threshold)
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{
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int offset = (PROP_BOX_SIZE * a) * grid_len + i * grid_w + j;
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float *in_ptr = input + offset;
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float box_x = *in_ptr * 2.0 - 0.5;
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float box_y = in_ptr[grid_len] * 2.0 - 0.5;
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float box_w = in_ptr[2 * grid_len] * 2.0;
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float box_h = in_ptr[3 * grid_len] * 2.0;
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box_x = (box_x + j) * (float)stride;
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box_y = (box_y + i) * (float)stride;
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box_w = box_w * box_w * (float)anchor[a * 2];
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box_h = box_h * box_h * (float)anchor[a * 2 + 1];
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box_x -= (box_w / 2.0);
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box_y -= (box_h / 2.0);
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float maxClassProbs = in_ptr[5 * grid_len];
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int maxClassId = 0;
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for (int k = 1; k < OBJ_CLASS_REAL_NUM; ++k)
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{
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float prob = in_ptr[(5 + k) * grid_len];
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if (prob > maxClassProbs)
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{
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maxClassId = k;
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maxClassProbs = prob;
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}
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}
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if (maxClassProbs > threshold)
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{
|
|
objProbs.push_back(maxClassProbs * box_confidence);
|
|
classId.push_back(maxClassId);
|
|
validCount++;
|
|
boxes.push_back(box_x);
|
|
boxes.push_back(box_y);
|
|
boxes.push_back(box_w);
|
|
boxes.push_back(box_h);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
return validCount;
|
|
}
|
|
|
|
int readLinesCode(const char *fileName, int lines[], int max_line)
|
|
{
|
|
FILE *file = fopen(fileName, "r");
|
|
char *s;
|
|
int i = 0;
|
|
int n = 0;
|
|
int result = 0;
|
|
while ((s = readLine(file, s, &n)) != NULL)
|
|
{
|
|
result = atoi(s);
|
|
lines[i++] = result;
|
|
if (i >= max_line)
|
|
break;
|
|
}
|
|
return i;
|
|
}
|
|
|
|
int loadCodes(const char *locationFilename, int label[])
|
|
{
|
|
for (int i = 0; i < OBJ_MAX_CLASS_NUM; i++)
|
|
{
|
|
codes[i] = i;
|
|
}
|
|
printf("load codes %s\n", locationFilename);
|
|
readLinesCode(locationFilename, label, OBJ_CLASS_REAL_NUM);
|
|
// PROP_BOX_SIZE = OBJ_CLASS_NUM + 5;
|
|
for (int i = 0; i < OBJ_CLASS_REAL_NUM; i++)
|
|
{
|
|
printf("%d => %d\n", i, codes[i]);
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
int post_process(rknn_app_context_t *app_ctx, void *outputs, letterbox_t *letter_box, float conf_threshold, float nms_threshold, object_detect_result_list *od_results)
|
|
{
|
|
#if defined(RV1106_1103)
|
|
rknn_tensor_mem **_outputs = (rknn_tensor_mem **)outputs;
|
|
#else
|
|
rknn_output *_outputs = (rknn_output *)outputs;
|
|
#endif
|
|
std::vector<float> filterBoxes;
|
|
std::vector<float> objProbs;
|
|
std::vector<int> classId;
|
|
int validCount = 0;
|
|
int stride = 0;
|
|
int grid_h = 0;
|
|
int grid_w = 0;
|
|
int model_in_w = app_ctx->model_width;
|
|
int model_in_h = app_ctx->model_height;
|
|
|
|
memset(od_results, 0, sizeof(object_detect_result_list));
|
|
|
|
for (int i = 0; i < 3; i++)
|
|
{
|
|
|
|
#if defined(RV1106_1103)
|
|
grid_h = app_ctx->output_attrs[i].dims[2];
|
|
grid_w = app_ctx->output_attrs[i].dims[3];
|
|
stride = model_in_h / grid_h;
|
|
//RV1106 only support i8
|
|
if (app_ctx->is_quant) {
|
|
validCount += process_i8((int8_t *)(_outputs[i]->virt_addr), (int *)anchor[i], grid_h, grid_w, model_in_h, model_in_w, stride, filterBoxes, objProbs,
|
|
classId, conf_threshold, app_ctx->output_attrs[i].zp, app_ctx->output_attrs[i].scale);
|
|
}
|
|
#elif defined(RKNPU1)
|
|
// NCHW reversed: WHCN
|
|
grid_h = app_ctx->output_attrs[i].dims[1];
|
|
grid_w = app_ctx->output_attrs[i].dims[0];
|
|
stride = model_in_h / grid_h;
|
|
/*
|
|
if (app_ctx->is_quant)
|
|
{
|
|
validCount += process_u8((uint8_t *)_outputs[i].buf, (int *)anchor[i], grid_h, grid_w, model_in_h, model_in_w, stride, filterBoxes, objProbs,
|
|
classId, conf_threshold, app_ctx->output_attrs[i].zp, app_ctx->output_attrs[i].scale);
|
|
}
|
|
else
|
|
{
|
|
validCount += process_fp32((float *)_outputs[i].buf, (int *)anchor[i], grid_h, grid_w, model_in_h, model_in_w, stride, filterBoxes, objProbs,
|
|
classId, conf_threshold);
|
|
}
|
|
*/
|
|
validCount += process_fp32((float *)_outputs[i].buf, (int *)anchor[i], grid_h, grid_w, model_in_h, model_in_w, stride, filterBoxes, objProbs,
|
|
classId, conf_threshold);
|
|
#else
|
|
grid_h = app_ctx->output_attrs[i].dims[2];
|
|
grid_w = app_ctx->output_attrs[i].dims[3];
|
|
stride = model_in_h / grid_h;
|
|
if (app_ctx->is_quant)
|
|
{
|
|
validCount += process_i8((int8_t *)_outputs[i].buf, (int *)anchor[i], grid_h, grid_w, model_in_h, model_in_w, stride, filterBoxes, objProbs,
|
|
classId, conf_threshold, app_ctx->output_attrs[i].zp, app_ctx->output_attrs[i].scale);
|
|
}
|
|
else
|
|
{
|
|
validCount += process_fp32((float *)_outputs[i].buf, (int *)anchor[i], grid_h, grid_w, model_in_h, model_in_w, stride, filterBoxes, objProbs,
|
|
classId, conf_threshold);
|
|
}
|
|
#endif
|
|
}
|
|
|
|
// no object detect
|
|
if (validCount <= 0)
|
|
{
|
|
return 0;
|
|
}
|
|
std::vector<int> indexArray;
|
|
for (int i = 0; i < validCount; ++i)
|
|
{
|
|
indexArray.push_back(i);
|
|
}
|
|
quick_sort_indice_inverse(objProbs, 0, validCount - 1, indexArray);
|
|
|
|
std::set<int> class_set(std::begin(classId), std::end(classId));
|
|
|
|
for (auto c : class_set)
|
|
{
|
|
nms(validCount, filterBoxes, classId, indexArray, c, nms_threshold);
|
|
}
|
|
|
|
int last_count = 0;
|
|
od_results->count = 0;
|
|
|
|
/* box valid detect target */
|
|
for (int i = 0; i < validCount; ++i)
|
|
{
|
|
if (indexArray[i] == -1 || last_count >= OBJ_NUMB_MAX_SIZE)
|
|
{
|
|
continue;
|
|
}
|
|
int n = indexArray[i];
|
|
|
|
float x1 = filterBoxes[n * 4 + 0] - letter_box->x_pad;
|
|
float y1 = filterBoxes[n * 4 + 1] - letter_box->y_pad;
|
|
float x2 = x1 + filterBoxes[n * 4 + 2];
|
|
float y2 = y1 + filterBoxes[n * 4 + 3];
|
|
int id = classId[n];
|
|
float obj_conf = objProbs[i];
|
|
|
|
od_results->results[last_count].box.left = (int)(clamp(x1, 0, model_in_w) / letter_box->scale);
|
|
od_results->results[last_count].box.top = (int)(clamp(y1, 0, model_in_h) / letter_box->scale);
|
|
od_results->results[last_count].box.right = (int)(clamp(x2, 0, model_in_w) / letter_box->scale);
|
|
od_results->results[last_count].box.bottom = (int)(clamp(y2, 0, model_in_h) / letter_box->scale);
|
|
od_results->results[last_count].prop = obj_conf;
|
|
od_results->results[last_count].cls_id = id;
|
|
last_count++;
|
|
}
|
|
od_results->count = last_count;
|
|
return 0;
|
|
}
|
|
|
|
int init_post_process()
|
|
{
|
|
|
|
char txt_path[128];
|
|
snprintf(txt_path, 127, "%s/%s.txt", MODELDIR, global_model);
|
|
|
|
int ret = 0;
|
|
ret = loadLabelName(txt_path, labels);
|
|
if (ret < 0)
|
|
{
|
|
printf("Load %s failed!\n", txt_path);
|
|
return -1;
|
|
}
|
|
|
|
snprintf(txt_path, 127, "%s/%s_code.txt", MODELDIR, global_model);
|
|
ret = loadCodes(txt_path, codes);
|
|
|
|
return 0;
|
|
}
|
|
|
|
const char *coco_cls_to_name(int cls_id)
|
|
{
|
|
|
|
if (cls_id >= OBJ_CLASS_REAL_NUM)
|
|
{
|
|
return "null";
|
|
}
|
|
|
|
if (labels[cls_id])
|
|
{
|
|
return labels[cls_id];
|
|
}
|
|
|
|
return "null";
|
|
}
|
|
|
|
void deinit_post_process()
|
|
{
|
|
for (int i = 0; i < OBJ_CLASS_REAL_NUM; i++)
|
|
{
|
|
if (labels[i] != nullptr)
|
|
{
|
|
free(labels[i]);
|
|
labels[i] = nullptr;
|
|
}
|
|
}
|
|
}
|