382 lines
10 KiB
C
382 lines
10 KiB
C
#include <stdlib.h>
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#include <math.h>
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#include <stdio.h>
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#include "region_layer.h"
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typedef struct
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{
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float x;
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float y;
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float w;
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float h;
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} box_t;
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typedef struct
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{
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int index;
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int class;
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float **probs;
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} sortable_box_t;
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int region_layer_init(region_layer_t *rl, int width, int height, int channels, int origin_width, int origin_height)
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{
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int flag = 0;
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rl->coords = 4;
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rl->image_width = 320;
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rl->image_height = 240;
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rl->classes = channels / 5 - 5;
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rl->net_width = origin_width;
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rl->net_height = origin_height;
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rl->layer_width = width;
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rl->layer_height = height;
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rl->boxes_number = (rl->layer_width * rl->layer_height * rl->anchor_number);
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rl->output_number = (rl->boxes_number * (rl->classes + rl->coords + 1));
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rl->output = malloc(rl->output_number * sizeof(float));
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if (rl->output == NULL)
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{
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flag = -1;
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goto malloc_error;
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}
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rl->boxes = malloc(rl->boxes_number * sizeof(box_t));
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if (rl->boxes == NULL)
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{
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flag = -2;
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goto malloc_error;
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}
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rl->probs_buf = malloc(rl->boxes_number * (rl->classes + 1) * sizeof(float));
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if (rl->probs_buf == NULL)
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{
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flag = -3;
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goto malloc_error;
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}
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rl->probs = malloc(rl->boxes_number * sizeof(float *));
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if (rl->probs == NULL)
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{
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flag = -4;
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goto malloc_error;
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}
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for (uint32_t i = 0; i < rl->boxes_number; i++)
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rl->probs[i] = &(rl->probs_buf[i * (rl->classes + 1)]);
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return 0;
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malloc_error:
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free(rl->output);
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free(rl->boxes);
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free(rl->probs_buf);
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free(rl->probs);
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return flag;
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}
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void region_layer_deinit(region_layer_t *rl)
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{
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free(rl->output);
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free(rl->boxes);
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free(rl->probs_buf);
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free(rl->probs);
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}
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static inline float sigmoid(float x)
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{
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return 1.f / (1.f + expf(-x));
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}
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static void activate_array(region_layer_t *rl, int index, int n)
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{
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float *output = &rl->output[index];
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float *input = &rl->input[index];
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for (int i = 0; i < n; ++i)
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output[i] = sigmoid(input[i]);
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}
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static int entry_index(region_layer_t *rl, int location, int entry)
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{
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int wh = rl->layer_width * rl->layer_height;
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int n = location / wh;
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int loc = location % wh;
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return n * wh * (rl->coords + rl->classes + 1) + entry * wh + loc;
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}
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static void softmax(region_layer_t *rl, float *input, int n, int stride, float *output)
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{
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int i;
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float diff;
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float e;
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float sum = 0;
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float largest_i = input[0];
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for (i = 0; i < n; ++i)
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{
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if (input[i * stride] > largest_i)
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largest_i = input[i * stride];
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}
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for (i = 0; i < n; ++i) {
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diff = input[i * stride] - largest_i;
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e = expf(diff);
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sum += e;
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output[i * stride] = e;
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}
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for (i = 0; i < n; ++i)
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output[i * stride] /= sum;
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}
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static void softmax_cpu(region_layer_t *rl, float *input, int n, int batch, int batch_offset, int groups, int stride, float *output)
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{
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int g, b;
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for (b = 0; b < batch; ++b) {
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for (g = 0; g < groups; ++g)
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softmax(rl, input + b * batch_offset + g, n, stride, output + b * batch_offset + g);
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}
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}
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static void forward_region_layer(region_layer_t *rl)
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{
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int index;
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for (index = 0; index < rl->output_number; index++)
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rl->output[index] = rl->input[index];
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for (int n = 0; n < rl->anchor_number; ++n)
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{
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index = entry_index(rl, n * rl->layer_width * rl->layer_height, 0);
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activate_array(rl, index, 2 * rl->layer_width * rl->layer_height);
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index = entry_index(rl, n * rl->layer_width * rl->layer_height, 4);
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activate_array(rl, index, rl->layer_width * rl->layer_height);
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}
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index = entry_index(rl, 0, rl->coords + 1);
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softmax_cpu(rl, rl->input + index, rl->classes, rl->anchor_number,
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rl->output_number / rl->anchor_number, rl->layer_width * rl->layer_height,
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rl->layer_width * rl->layer_height, rl->output + index);
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}
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static void correct_region_boxes(region_layer_t *rl, box_t *boxes)
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{
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uint32_t net_width = rl->net_width;
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uint32_t net_height = rl->net_height;
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uint32_t image_width = rl->image_width;
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uint32_t image_height = rl->image_height;
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uint32_t boxes_number = rl->boxes_number;
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int new_w = 0;
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int new_h = 0;
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if (((float)net_width / image_width) <
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((float)net_height / image_height)) {
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new_w = net_width;
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new_h = (image_height * net_width) / image_width;
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} else {
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new_h = net_height;
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new_w = (image_width * net_height) / image_height;
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}
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for (int i = 0; i < boxes_number; ++i) {
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box_t b = boxes[i];
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b.x = (b.x - (net_width - new_w) / 2. / net_width) /
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((float)new_w / net_width);
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b.y = (b.y - (net_height - new_h) / 2. / net_height) /
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((float)new_h / net_height);
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b.w *= (float)net_width / new_w;
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b.h *= (float)net_height / new_h;
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boxes[i] = b;
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}
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}
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static box_t get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h, int stride)
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{
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volatile box_t b;
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b.x = (i + x[index + 0 * stride]) / w;
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b.y = (j + x[index + 1 * stride]) / h;
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b.w = expf(x[index + 2 * stride]) * biases[2 * n] / w;
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b.h = expf(x[index + 3 * stride]) * biases[2 * n + 1] / h;
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return b;
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}
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static void get_region_boxes(region_layer_t *rl, float *predictions, float **probs, box_t *boxes)
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{
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uint32_t layer_width = rl->layer_width;
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uint32_t layer_height = rl->layer_height;
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uint32_t anchor_number = rl->anchor_number;
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uint32_t classes = rl->classes;
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uint32_t coords = rl->coords;
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float threshold = rl->threshold;
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for (int i = 0; i < layer_width * layer_height; ++i)
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{
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int row = i / layer_width;
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int col = i % layer_width;
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for (int n = 0; n < anchor_number; ++n)
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{
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int index = n * layer_width * layer_height + i;
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for (int j = 0; j < classes; ++j)
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probs[index][j] = 0;
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int obj_index = entry_index(rl, n * layer_width * layer_height + i, coords);
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int box_index = entry_index(rl, n * layer_width * layer_height + i, 0);
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float scale = predictions[obj_index];
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boxes[index] = get_region_box(predictions, rl->anchor, n, box_index, col, row,
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layer_width, layer_height, layer_width * layer_height);
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float max = 0;
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for (int j = 0; j < classes; ++j)
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{
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int class_index = entry_index(rl, n * layer_width * layer_height + i, coords + 1 + j);
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float prob = scale * predictions[class_index];
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probs[index][j] = (prob > threshold) ? prob : 0;
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if (prob > max)
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max = prob;
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}
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probs[index][classes] = max;
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}
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}
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correct_region_boxes(rl, boxes);
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}
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static int nms_comparator(void *pa, void *pb)
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{
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sortable_box_t a = *(sortable_box_t *)pa;
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sortable_box_t b = *(sortable_box_t *)pb;
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float diff = a.probs[a.index][b.class] - b.probs[b.index][b.class];
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if (diff < 0)
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return 1;
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else if (diff > 0)
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return -1;
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return 0;
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}
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static float overlap(float x1, float w1, float x2, float w2)
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{
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float l1 = x1 - w1/2;
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float l2 = x2 - w2/2;
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float left = l1 > l2 ? l1 : l2;
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float r1 = x1 + w1/2;
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float r2 = x2 + w2/2;
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float right = r1 < r2 ? r1 : r2;
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return right - left;
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}
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static float box_intersection(box_t a, box_t b)
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{
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float w = overlap(a.x, a.w, b.x, b.w);
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float h = overlap(a.y, a.h, b.y, b.h);
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if (w < 0 || h < 0)
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return 0;
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return w * h;
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}
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static float box_union(box_t a, box_t b)
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{
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float i = box_intersection(a, b);
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float u = a.w * a.h + b.w * b.h - i;
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return u;
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}
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static float box_iou(box_t a, box_t b)
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{
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return box_intersection(a, b) / box_union(a, b);
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}
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static void do_nms_sort(region_layer_t *rl, box_t *boxes, float **probs)
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{
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uint32_t boxes_number = rl->boxes_number;
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uint32_t classes = rl->classes;
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float nms_value = rl->nms_value;
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int i, j, k;
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sortable_box_t s[boxes_number];
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for (i = 0; i < boxes_number; ++i)
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{
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s[i].index = i;
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s[i].class = 0;
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s[i].probs = probs;
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}
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for (k = 0; k < classes; ++k)
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{
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for (i = 0; i < boxes_number; ++i)
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s[i].class = k;
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qsort(s, boxes_number, sizeof(sortable_box_t), nms_comparator);
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for (i = 0; i < boxes_number; ++i)
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{
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if (probs[s[i].index][k] == 0)
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continue;
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box_t a = boxes[s[i].index];
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for (j = i + 1; j < boxes_number; ++j)
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{
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box_t b = boxes[s[j].index];
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if (box_iou(a, b) > nms_value)
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probs[s[j].index][k] = 0;
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}
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}
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}
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}
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static int max_index(float *a, int n)
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{
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int i, max_i = 0;
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float max = a[0];
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for (i = 1; i < n; ++i)
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{
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if (a[i] > max)
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{
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max = a[i];
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max_i = i;
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}
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}
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return max_i;
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}
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static void region_layer_output(region_layer_t *rl, obj_info_t *obj_info)
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{
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uint32_t obj_number = 0;
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uint32_t image_width = rl->image_width;
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uint32_t image_height = rl->image_height;
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uint32_t boxes_number = rl->boxes_number;
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float threshold = rl->threshold;
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box_t *boxes = (box_t *)rl->boxes;
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for (int i = 0; i < rl->boxes_number; ++i)
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{
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int class = max_index(rl->probs[i], rl->classes);
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float prob = rl->probs[i][class];
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if (prob > threshold)
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{
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box_t *b = boxes + i;
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obj_info->obj[obj_number].x1 = b->x * image_width - (b->w * image_width / 2);
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obj_info->obj[obj_number].y1 = b->y * image_height - (b->h * image_height / 2);
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obj_info->obj[obj_number].x2 = b->x * image_width + (b->w * image_width / 2);
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obj_info->obj[obj_number].y2 = b->y * image_height + (b->h * image_height / 2);
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obj_info->obj[obj_number].class_id = class;
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obj_info->obj[obj_number].prob = prob;
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obj_number++;
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}
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}
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obj_info->obj_number = obj_number;
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}
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void region_layer_run(region_layer_t *rl, obj_info_t *obj_info)
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{
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forward_region_layer(rl);
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get_region_boxes(rl, rl->output, rl->probs, rl->boxes);
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do_nms_sort(rl, rl->boxes, rl->probs);
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region_layer_output(rl, obj_info);
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}
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