683 lines
26 KiB
C
683 lines
26 KiB
C
#pragma once
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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 <math.h>
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#include <assert.h>
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#if CONFIG_SPIRAM_SUPPORT
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#include "freertos/FreeRTOS.h"
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#endif
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#ifndef max
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#define max(x, y) (((x) < (y)) ? (y) : (x))
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#endif
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#ifndef min
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#define min(x, y) (((x) < (y)) ? (x) : (y))
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#endif
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typedef float fptp_t;
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typedef uint8_t uc_t;
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typedef enum
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{
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DL_SUCCESS = 0,
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DL_FAIL = 1,
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} dl_error_type;
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typedef enum
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{
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PADDING_VALID = 0,
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PADDING_SAME = 1,
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PADDING_SAME_DONT_FREE_INPUT = 2,
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PADDING_SAME_MXNET = 3,
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} dl_padding_type;
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typedef enum
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{
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DL_POOLING_MAX = 0,
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DL_POOLING_AVG = 1,
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} dl_pooling_type;
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/*
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* Matrix for 3d
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* @Warning: the sequence of variables is fixed, cannot be modified, otherwise there will be errors in esp_dsp_dot_float
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*/
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typedef struct
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{
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int w; /*!< Width */
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int h; /*!< Height */
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int c; /*!< Channel */
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int n; /*!< Number of filter, input and output must be 1 */
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int stride; /*!< Step between lines */
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fptp_t *item; /*!< Data */
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} dl_matrix3d_t;
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typedef struct
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{
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int w; /*!< Width */
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int h; /*!< Height */
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int c; /*!< Channel */
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int n; /*!< Number of filter, input and output must be 1 */
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int stride; /*!< Step between lines */
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uc_t *item; /*!< Data */
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} dl_matrix3du_t;
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typedef struct
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{
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int stride_x;
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int stride_y;
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dl_padding_type padding;
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} dl_matrix3d_mobilenet_config_t;
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/*
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* @brief Allocate a zero-initialized space. Must use 'dl_lib_free' to free the memory.
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*
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* @param cnt Count of units.
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* @param size Size of unit.
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* @param align Align of memory. If not required, set 0.
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* @return Pointer of allocated memory. Null for failed.
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*/
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static inline void *dl_lib_calloc(int cnt, int size, int align)
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{
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int total_size = cnt * size + align + sizeof(void *);
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void *res = malloc(total_size);
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if (NULL == res)
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{
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#if CONFIG_SPIRAM_SUPPORT
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res = heap_caps_malloc(total_size, MALLOC_CAP_8BIT | MALLOC_CAP_SPIRAM);
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}
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if (NULL == res)
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{
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printf("Item psram alloc failed. Size: %d x %d\n", cnt, size);
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#else
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printf("Item alloc failed. Size: %d x %d\n", cnt, size);
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#endif
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return NULL;
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}
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bzero(res, total_size);
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void **data = (void **)res + 1;
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void **aligned;
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if (align)
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aligned = (void **)(((size_t)data + (align - 1)) & -align);
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else
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aligned = data;
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aligned[-1] = res;
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return (void *)aligned;
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}
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static inline void dl_lib_free(void *d)
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{
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if (NULL == d)
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return;
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free(((void **)d)[-1]);
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}
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/*
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* @brief Allocate a 3D matrix with float items, the access sequence is NHWC
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*
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* @param n Number of matrix3d, for filters it is out channels, for others it is 1
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* @param w Width of matrix3d
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* @param h Height of matrix3d
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* @param c Channel of matrix3d
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* @return 3d matrix
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*/
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static inline dl_matrix3d_t *dl_matrix3d_alloc(int n, int w, int h, int c)
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{
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dl_matrix3d_t *r = (dl_matrix3d_t *)dl_lib_calloc(1, sizeof(dl_matrix3d_t), 0);
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if (NULL == r)
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{
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printf("internal r failed.\n");
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return NULL;
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}
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fptp_t *items = (fptp_t *)dl_lib_calloc(n * w * h * c, sizeof(fptp_t), 0);
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if (NULL == items)
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{
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printf("matrix3d item alloc failed.\n");
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dl_lib_free(r);
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return NULL;
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}
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r->w = w;
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r->h = h;
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r->c = c;
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r->n = n;
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r->stride = w * c;
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r->item = items;
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return r;
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}
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/*
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* @brief Allocate a 3D matrix with 8-bits items, the access sequence is NHWC
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*
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* @param n Number of matrix3d, for filters it is out channels, for others it is 1
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* @param w Width of matrix3d
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* @param h Height of matrix3d
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* @param c Channel of matrix3d
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* @return 3d matrix
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*/
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static inline dl_matrix3du_t *dl_matrix3du_alloc(int n, int w, int h, int c)
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{
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dl_matrix3du_t *r = (dl_matrix3du_t *)dl_lib_calloc(1, sizeof(dl_matrix3du_t), 0);
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if (NULL == r)
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{
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printf("internal r failed.\n");
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return NULL;
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}
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uc_t *items = (uc_t *)dl_lib_calloc(n * w * h * c, sizeof(uc_t), 0);
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if (NULL == items)
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{
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printf("matrix3du item alloc failed.\n");
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dl_lib_free(r);
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return NULL;
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}
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r->w = w;
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r->h = h;
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r->c = c;
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r->n = n;
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r->stride = w * c;
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r->item = items;
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return r;
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}
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/*
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* @brief Free a matrix3d
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*
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* @param m matrix3d with float items
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*/
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static inline void dl_matrix3d_free(dl_matrix3d_t *m)
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{
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if (NULL == m)
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return;
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if (NULL == m->item)
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{
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dl_lib_free(m);
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return;
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}
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dl_lib_free(m->item);
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dl_lib_free(m);
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}
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/*
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* @brief Free a matrix3d
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*
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* @param m matrix3d with 8-bits items
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*/
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static inline void dl_matrix3du_free(dl_matrix3du_t *m)
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{
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if (NULL == m)
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return;
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if (NULL == m->item)
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{
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dl_lib_free(m);
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return;
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}
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dl_lib_free(m->item);
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dl_lib_free(m);
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}
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/*
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* @brief Dot product with a vector and matrix
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*
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* @param out Space to put the result
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* @param in input vector
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* @param f filter matrix
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*/
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void dl_matrix3dff_dot_product(dl_matrix3d_t *out, dl_matrix3d_t *in, dl_matrix3d_t *f);
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/**
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* @brief Do a softmax operation on a matrix3d
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*
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* @param in Input matrix3d
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*/
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void dl_matrix3d_softmax(dl_matrix3d_t *m);
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/**
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* @brief Copy a range of float items from an existing matrix to a preallocated matrix
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*
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* @param dst The destination slice matrix
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* @param src The source matrix to slice
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* @param x X-offset of the origin of the returned matrix within the sliced matrix
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* @param y Y-offset of the origin of the returned matrix within the sliced matrix
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* @param w Width of the resulting matrix
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* @param h Height of the resulting matrix
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*/
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void dl_matrix3d_slice_copy(dl_matrix3d_t *dst,
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dl_matrix3d_t *src,
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int x,
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int y,
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int w,
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int h);
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/**
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* @brief Copy a range of 8-bits items from an existing matrix to a preallocated matrix
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*
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* @param dst The destination slice matrix
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* @param src The source matrix to slice
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* @param x X-offset of the origin of the returned matrix within the sliced matrix
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* @param y Y-offset of the origin of the returned matrix within the sliced matrix
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* @param w Width of the resulting matrix
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* @param h Height of the resulting matrix
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*/
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void dl_matrix3du_slice_copy(dl_matrix3du_t *dst,
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dl_matrix3du_t *src,
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int x,
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int y,
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int w,
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int h);
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/**
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* @brief Transform a sliced matrix block from nhwc to nchw, the block needs to be memory continous.
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*
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* @param out The destination sliced matrix in nchw
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* @param in The source sliced matrix in nhwc
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*/
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void dl_matrix3d_sliced_transform_nchw(dl_matrix3d_t *out,
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dl_matrix3d_t *in);
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/**
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* @brief Do a general CNN layer pass, dimension is (number, width, height, channel)
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*
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* @param in Input matrix3d
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* @param filter Weights of the neurons
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* @param bias Bias for the CNN layer
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* @param stride_x The step length of the convolution window in x(width) direction
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* @param stride_y The step length of the convolution window in y(height) direction
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* @param padding One of VALID or SAME
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* @param mode Do convolution using C implement or xtensa implement, 0 or 1, with respect
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* If ESP_PLATFORM is not defined, this value is not used. Default is 0
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* @return The result of CNN layer
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*/
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dl_matrix3d_t *dl_matrix3d_conv(dl_matrix3d_t *in,
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dl_matrix3d_t *filter,
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dl_matrix3d_t *bias,
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int stride_x,
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int stride_y,
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int padding,
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int mode);
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/**
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* @brief Do a global average pooling layer pass, dimension is (number, width, height, channel)
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*
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* @param in Input matrix3d
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*
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* @return The result of global average pooling layer
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*/
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dl_matrix3d_t *dl_matrix3d_global_pool(dl_matrix3d_t *in);
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/**
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* @brief Calculate pooling layer of a feature map
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*
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* @param in Input matrix, size (1, w, h, c)
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* @param f_w Window width
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* @param f_h Window height
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* @param stride_x Stride in horizontal direction
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* @param stride_y Stride in vertical direction
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* @param padding Padding type: PADDING_VALID and PADDING_SAME
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* @param pooling_type Pooling type: DL_POOLING_MAX and POOLING_AVG
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* @return Resulting matrix, size (1, w', h', c)
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*/
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dl_matrix3d_t *dl_matrix3d_pooling(dl_matrix3d_t *in,
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int f_w,
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int f_h,
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int stride_x,
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int stride_y,
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dl_padding_type padding,
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dl_pooling_type pooling_type);
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/**
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* @brief Do a batch normalization operation, update the input matrix3d: input = input * scale + offset
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*
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* @param m Input matrix3d
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* @param scale scale matrix3d, scale = gamma/((moving_variance+sigma)^(1/2))
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* @param Offset Offset matrix3d, offset = beta-(moving_mean*gamma/((moving_variance+sigma)^(1/2)))
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*/
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void dl_matrix3d_batch_normalize(dl_matrix3d_t *m,
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dl_matrix3d_t *scale,
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dl_matrix3d_t *offset);
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/**
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* @brief Add a pair of matrix3d item-by-item: res=in_1+in_2
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*
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* @param in_1 First Floating point input matrix3d
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* @param in_2 Second Floating point input matrix3d
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*
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* @return Added data
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*/
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dl_matrix3d_t *dl_matrix3d_add(dl_matrix3d_t *in_1, dl_matrix3d_t *in_2);
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/**
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* @brief Concatenate the channels of two matrix3ds into a new matrix3d
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*
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* @param in_1 First Floating point input matrix3d
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* @param in_2 Second Floating point input matrix3d
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*
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* @return A newly allocated matrix3d with as avlues in_1|in_2
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*/
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dl_matrix3d_t *dl_matrix3d_concat(dl_matrix3d_t *in_1, dl_matrix3d_t *in_2);
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/**
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* @brief Concatenate the channels of four matrix3ds into a new matrix3d
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*
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* @param in_1 First Floating point input matrix3d
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* @param in_2 Second Floating point input matrix3d
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* @param in_3 Third Floating point input matrix3d
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* @param in_4 Fourth Floating point input matrix3d
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*
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* @return A newly allocated matrix3d with as avlues in_1|in_2|in_3|in_4
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*/
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dl_matrix3d_t *dl_matrix3d_concat_4(dl_matrix3d_t *in_1,
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dl_matrix3d_t *in_2,
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dl_matrix3d_t *in_3,
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dl_matrix3d_t *in_4);
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/**
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* @brief Concatenate the channels of eight matrix3ds into a new matrix3d
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*
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* @param in_1 First Floating point input matrix3d
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* @param in_2 Second Floating point input matrix3d
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* @param in_3 Third Floating point input matrix3d
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* @param in_4 Fourth Floating point input matrix3d
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* @param in_5 Fifth Floating point input matrix3d
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* @param in_6 Sixth Floating point input matrix3d
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* @param in_7 Seventh Floating point input matrix3d
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* @param in_8 eighth Floating point input matrix3d
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*
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* @return A newly allocated matrix3d with as avlues in_1|in_2|in_3|in_4|in_5|in_6|in_7|in_8
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*/
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dl_matrix3d_t *dl_matrix3d_concat_8(dl_matrix3d_t *in_1,
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dl_matrix3d_t *in_2,
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dl_matrix3d_t *in_3,
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dl_matrix3d_t *in_4,
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dl_matrix3d_t *in_5,
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dl_matrix3d_t *in_6,
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dl_matrix3d_t *in_7,
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dl_matrix3d_t *in_8);
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/**
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* @brief Do a mobilefacenet block forward, dimension is (number, width, height, channel)
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*
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* @param in Input matrix3d
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* @param pw Weights of the pointwise conv layer
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* @param pw_bn_scale The scale params of the batch_normalize layer after the pointwise conv layer
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* @param pw_bn_offset The offset params of the batch_normalize layer after the pointwise conv layer
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* @param dw Weights of the depthwise conv layer
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* @param dw_bn_scale The scale params of the batch_normalize layer after the depthwise conv layer
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* @param dw_bn_offset The offset params of the batch_normalize layer after the depthwise conv layer
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* @param pw_linear Weights of the pointwise linear conv layer
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* @param pw_linear_bn_scale The scale params of the batch_normalize layer after the pointwise linear conv layer
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* @param pw_linear_bn_offset The offset params of the batch_normalize layer after the pointwise linear conv layer
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* @param stride_x The step length of the convolution window in x(width) direction
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* @param stride_y The step length of the convolution window in y(height) direction
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* @param padding One of VALID or SAME
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* @param mode Do convolution using C implement or xtensa implement, 0 or 1, with respect
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* If ESP_PLATFORM is not defined, this value is not used. Default is 0
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* @return The result of a mobilefacenet block
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*/
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dl_matrix3d_t *dl_matrix3d_mobilefaceblock(dl_matrix3d_t *in,
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dl_matrix3d_t *pw,
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dl_matrix3d_t *pw_bn_scale,
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dl_matrix3d_t *pw_bn_offset,
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dl_matrix3d_t *dw,
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dl_matrix3d_t *dw_bn_scale,
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dl_matrix3d_t *dw_bn_offset,
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dl_matrix3d_t *pw_linear,
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dl_matrix3d_t *pw_linear_bn_scale,
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dl_matrix3d_t *pw_linear_bn_offset,
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int stride_x,
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int stride_y,
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int padding,
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int mode,
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int shortcut);
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/**
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* @brief Do a mobilefacenet block forward with 1x1 split conv, dimension is (number, width, height, channel)
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*
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* @param in Input matrix3d
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* @param pw_1 Weights of the pointwise conv layer 1
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* @param pw_2 Weights of the pointwise conv layer 2
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* @param pw_bn_scale The scale params of the batch_normalize layer after the pointwise conv layer
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* @param pw_bn_offset The offset params of the batch_normalize layer after the pointwise conv layer
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* @param dw Weights of the depthwise conv layer
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* @param dw_bn_scale The scale params of the batch_normalize layer after the depthwise conv layer
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* @param dw_bn_offset The offset params of the batch_normalize layer after the depthwise conv layer
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* @param pw_linear_1 Weights of the pointwise linear conv layer 1
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* @param pw_linear_2 Weights of the pointwise linear conv layer 2
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* @param pw_linear_bn_scale The scale params of the batch_normalize layer after the pointwise linear conv layer
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* @param pw_linear_bn_offset The offset params of the batch_normalize layer after the pointwise linear conv layer
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* @param stride_x The step length of the convolution window in x(width) direction
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* @param stride_y The step length of the convolution window in y(height) direction
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* @param padding One of VALID or SAME
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* @param mode Do convolution using C implement or xtensa implement, 0 or 1, with respect
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* If ESP_PLATFORM is not defined, this value is not used. Default is 0
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* @return The result of a mobilefacenet block
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*/
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dl_matrix3d_t *dl_matrix3d_mobilefaceblock_split(dl_matrix3d_t *in,
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dl_matrix3d_t *pw_1,
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dl_matrix3d_t *pw_2,
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dl_matrix3d_t *pw_bn_scale,
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dl_matrix3d_t *pw_bn_offset,
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dl_matrix3d_t *dw,
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dl_matrix3d_t *dw_bn_scale,
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dl_matrix3d_t *dw_bn_offset,
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dl_matrix3d_t *pw_linear_1,
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dl_matrix3d_t *pw_linear_2,
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dl_matrix3d_t *pw_linear_bn_scale,
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dl_matrix3d_t *pw_linear_bn_offset,
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int stride_x,
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int stride_y,
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int padding,
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int mode,
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int shortcut);
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void dl_matrix3d_init_bias(dl_matrix3d_t *out, dl_matrix3d_t *bias);
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void dl_matrix3d_multiply(dl_matrix3d_t *out, dl_matrix3d_t *in1, dl_matrix3d_t *in2);
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//
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// Activation
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//
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/**
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* @brief Do a standard relu operation, update the input matrix3d
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*
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* @param m Floating point input matrix3d
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*/
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void dl_matrix3d_relu(dl_matrix3d_t *m);
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/**
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* @brief Do a relu (Rectifier Linear Unit) operation, update the input matrix3d
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*
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* @param in Floating point input matrix3d
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* @param clip If value is higher than this, it will be clipped to this value
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*/
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void dl_matrix3d_relu_clip(dl_matrix3d_t *m, fptp_t clip);
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/**
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* @brief Do a Prelu (Rectifier Linear Unit) operation, update the input matrix3d
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*
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* @param in Floating point input matrix3d
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* @param alpha If value is less than zero, it will be updated by multiplying this factor
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*/
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void dl_matrix3d_p_relu(dl_matrix3d_t *in, dl_matrix3d_t *alpha);
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/**
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* @brief Do a leaky relu (Rectifier Linear Unit) operation, update the input matrix3d
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*
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* @param in Floating point input matrix3d
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* @param alpha If value is less than zero, it will be updated by multiplying this factor
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*/
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void dl_matrix3d_leaky_relu(dl_matrix3d_t *m, fptp_t alpha);
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//
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// Conv 1x1
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//
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void dl_matrix3dff_conv_1x1(dl_matrix3d_t *out,
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dl_matrix3d_t *in,
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dl_matrix3d_t *filter);
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void dl_matrix3dff_conv_1x1_with_bias(dl_matrix3d_t *out,
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dl_matrix3d_t *in,
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dl_matrix3d_t *filter,
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dl_matrix3d_t *bias);
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void dl_matrix3duf_conv_1x1(dl_matrix3d_t *out,
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dl_matrix3du_t *in,
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dl_matrix3d_t *filter);
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void dl_matrix3duf_conv_1x1_with_bias(dl_matrix3d_t *out,
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dl_matrix3du_t *in,
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dl_matrix3d_t *filter,
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dl_matrix3d_t *bias);
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//
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// Conv 3x3
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//
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void dl_matrix3dff_conv_3x3_op(dl_matrix3d_t *out,
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dl_matrix3d_t *in,
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dl_matrix3d_t *f,
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int step_x,
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int step_y);
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dl_matrix3d_t *dl_matrix3dff_conv_3x3(dl_matrix3d_t *in,
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dl_matrix3d_t *filter,
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dl_matrix3d_t *bias,
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int stride_x,
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int stride_y,
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dl_padding_type padding);
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//
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// Conv Common
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//
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dl_matrix3d_t *dl_matrix3duf_conv_common(dl_matrix3du_t *in,
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dl_matrix3d_t *filter,
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dl_matrix3d_t *bias,
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int stride_x,
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int stride_y,
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dl_padding_type padding);
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dl_matrix3d_t *dl_matrix3dff_conv_common(dl_matrix3d_t *in,
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dl_matrix3d_t *filter,
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dl_matrix3d_t *bias,
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int stride_x,
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int stride_y,
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dl_padding_type padding);
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//
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// Depthwise 3x3
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//
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|
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dl_matrix3d_t *dl_matrix3dff_depthwise_conv_3x3(dl_matrix3d_t *in,
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dl_matrix3d_t *filter,
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|
int stride_x,
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|
int stride_y,
|
|
int padding);
|
|
|
|
dl_matrix3d_t *dl_matrix3duf_depthwise_conv_3x3(dl_matrix3du_t *in,
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|
dl_matrix3d_t *filter,
|
|
int stride_x,
|
|
int stride_y,
|
|
int padding);
|
|
|
|
void dl_matrix3dff_depthwise_conv_3x3_op(dl_matrix3d_t *out,
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|
dl_matrix3d_t *in,
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dl_matrix3d_t *f,
|
|
int step_x,
|
|
int step_y);
|
|
|
|
//
|
|
// Depthwise Common
|
|
//
|
|
|
|
/**
|
|
* @brief Do a depthwise CNN layer pass, dimension is (number, width, height, channel)
|
|
*
|
|
* @param in Input matrix3d
|
|
* @param filter Weights of the neurons
|
|
* @param stride_x The step length of the convolution window in x(width) direction
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|
* @param stride_y The step length of the convolution window in y(height) direction
|
|
* @param padding One of VALID or SAME
|
|
* @param mode Do convolution using C implement or xtensa implement, 0 or 1, with respect
|
|
* If ESP_PLATFORM is not defined, this value is not used. Default is 0
|
|
* @return The result of depthwise CNN layer
|
|
*/
|
|
dl_matrix3d_t *dl_matrix3dff_depthwise_conv_common(dl_matrix3d_t *in,
|
|
dl_matrix3d_t *filter,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding);
|
|
|
|
//
|
|
// FC
|
|
//
|
|
/**
|
|
* @brief Do a general fully connected layer pass, dimension is (number, width, height, channel)
|
|
*
|
|
* @param in Input matrix3d, size is (1, w, 1, 1)
|
|
* @param filter Weights of the neurons, size is (1, w, h, 1)
|
|
* @param bias Bias for the fc layer, size is (1, 1, 1, h)
|
|
* @return The result of fc layer, size is (1, 1, 1, h)
|
|
*/
|
|
void dl_matrix3dff_fc(dl_matrix3d_t *out,
|
|
dl_matrix3d_t *in,
|
|
dl_matrix3d_t *filter);
|
|
|
|
void dl_matrix3dff_fc_with_bias(dl_matrix3d_t *out,
|
|
dl_matrix3d_t *in,
|
|
dl_matrix3d_t *filter,
|
|
dl_matrix3d_t *bias);
|
|
|
|
//
|
|
// Mobilenet
|
|
//
|
|
|
|
/**
|
|
* @brief Do a mobilenet block forward, dimension is (number, width, height, channel)
|
|
*
|
|
* @param in Input matrix3d
|
|
* @param filter Weights of the neurons
|
|
* @param stride_x The step length of the convolution window in x(width) direction
|
|
* @param stride_y The step length of the convolution window in y(height) direction
|
|
* @param padding One of VALID or SAME
|
|
* @param mode Do convolution using C implement or xtensa implement, 0 or 1, with respect
|
|
* If ESP_PLATFORM is not defined, this value is not used. Default is 0
|
|
* @return The result of depthwise CNN layer
|
|
*/
|
|
dl_matrix3d_t *dl_matrix3dff_mobilenet(dl_matrix3d_t *in,
|
|
dl_matrix3d_t *dilate_filter,
|
|
dl_matrix3d_t *dilate_prelu,
|
|
dl_matrix3d_t *depthwise_filter,
|
|
dl_matrix3d_t *depthwise_prelu,
|
|
dl_matrix3d_t *compress_filter,
|
|
dl_matrix3d_t *bias,
|
|
dl_matrix3d_mobilenet_config_t config);
|
|
|
|
/**
|
|
* @brief Do a mobilenet block forward, dimension is (number, width, height, channel)
|
|
*
|
|
* @param in Input matrix3du
|
|
* @param filter Weights of the neurons
|
|
* @param stride_x The step length of the convolution window in x(width) direction
|
|
* @param stride_y The step length of the convolution window in y(height) direction
|
|
* @param padding One of VALID or SAME
|
|
* @param mode Do convolution using C implement or xtensa implement, 0 or 1, with respect
|
|
* If ESP_PLATFORM is not defined, this value is not used. Default is 0
|
|
* @return The result of depthwise CNN layer
|
|
*/
|
|
dl_matrix3d_t *dl_matrix3duf_mobilenet(dl_matrix3du_t *in,
|
|
dl_matrix3d_t *dilate_filter,
|
|
dl_matrix3d_t *dilate_prelu,
|
|
dl_matrix3d_t *depthwise_filter,
|
|
dl_matrix3d_t *depthwise_prelu,
|
|
dl_matrix3d_t *compress_filter,
|
|
dl_matrix3d_t *bias,
|
|
dl_matrix3d_mobilenet_config_t config);
|