97 lines
3.5 KiB
C
97 lines
3.5 KiB
C
#include "ConvFuncs.h"
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void conv_func(int8_t* d_i, int8_t* weight, int32_t* bias, int32_t* d_o, int8_t di_type,
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int height, int width, int padding, int channel, int out_channel,
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int kernel_width, int kernel_height) {
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// 使用多重循环卷积(BUF_HEIGHT - KERNEL_SIZE + PADDING * 2) / STRIDE + 1
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int16_t* d_16 = (int16_t*)d_i;
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int32_t* d_32 = (int32_t*)d_i;
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int height_o = (height - kernel_height + padding * 2) + 1;
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int width_o = (width - kernel_width + padding * 2) + 1;
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for (int c = 0; c < out_channel; c++) {
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for (int h = -padding; h < (height - kernel_height + padding) + 1; h++) {
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for (int w = -padding; w < (width - kernel_width + padding) + 1; w++) {
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int32_t sum = 0;
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for(int k = 0; k < channel; k++) {
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for (int m = 0; m < kernel_height; m++) {
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for (int n = 0; n < kernel_width; n++) {
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int h_m = h + m;
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int w_n = w + n;
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if (h_m >= 0 && h_m < height && w_n >= 0 && w_n < width) {
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if(di_type == 0){
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sum += d_i[(k * height + h_m) * width + w_n] *
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weight[((c * channel + k) * kernel_height + m) * kernel_width + n];
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} else if (di_type == 1) {
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sum += d_16[(k * height + h_m) * width + w_n] *
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weight[((c * channel + k) * kernel_height + m) * kernel_width + n];
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} else if (di_type == 2) {
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sum += d_32[(k * height + h_m) * width + w_n] *
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weight[((c * channel + k) * kernel_height + m) * kernel_width + n];
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}
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}
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}
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}
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}
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d_o[c * height_o * width_o + (h+padding) * width_o + w + padding] = sum + bias[c];
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}
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}
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}
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}
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void pool_func(int32_t* d_i, int32_t* d_o, int channel, int height, int width) {
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for (int c = 0; c < channel; c++) {
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for (int h = 0; h < height / 2; h++) {
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for (int w = 0; w < width / 2; w++) {
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int32_t sum = d_i[(c * height + (h << 1)) * width + (w << 1)] +
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d_i[(c * height + (h << 1) + 1) * width + (w << 1)] +
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d_i[(c * height + (h << 1)) * width + (w << 1) + 1] +
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d_i[(c * height + (h << 1) + 1) * width + (w << 1) + 1];
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if(sum > 0) d_o[(c * height / 2 + h) * (width / 2) + w] = sum >> 2;
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else d_o[(c * height / 2 + h) * (width / 2) + w] = 0;
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}
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}
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}
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}
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void linear_func(
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int32_t* d_i,
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int8_t* weight,
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int32_t* bias,
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int32_t* output,
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int input_size,
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int output_size) {
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for (int i = 0; i < output_size; i++) {
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output[i] = bias[i];
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for (int j = 0; j < input_size; j++) {
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output[i] += d_i[j] * weight[i * input_size + j];
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}
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}
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}
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void relu_int(int32_t *x, int size) {
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for (int i = 0; i < size; i++) {
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if (x[i] < 0) {
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x[i] = 0;
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}
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}
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}
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void softmax(int32_t *x, float *output, int size) {
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int32_t max = x[0];
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for (int i = 1; i < size; i++) {
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if (x[i] > max) {
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max = x[i];
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}
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}
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float sum = 0;
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for (int i = 0; i < size; i++) {
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output[i] = exp((float)(x[i] - max)) ;
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sum += output[i];
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}
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for (int i = 0; i < size; i++) {
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output[i] /= sum;
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}
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} |