#pragma once
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <assert.h>
#if CONFIG_SPIRAM_SUPPORT || CONFIG_ESP32_SPIRAM_SUPPORT
#include "freertos/FreeRTOS.h"
#define DL_SPIRAM_SUPPORT 1
#else
#define DL_SPIRAM_SUPPORT 0
#endif
#ifndef max
#define max(x, y) (((x) < (y)) ? (y) : (x))
#endif
#ifndef min
#define min(x, y) (((x) < (y)) ? (x) : (y))
#endif
typedef float fptp_t;
typedef uint8_t uc_t;
typedef enum
{
DL_SUCCESS = 0,
DL_FAIL = 1,
} dl_error_type;
typedef enum
{
PADDING_VALID = 0, /*!< Valid padding */
PADDING_SAME = 1, /*!< Same padding, from right to left, free input */
PADDING_SAME_DONT_FREE_INPUT = 2, /*!< Same padding, from right to left, do not free input */
PADDING_SAME_MXNET = 3, /*!< Same padding, from left to right */
} dl_padding_type;
typedef enum
{
DL_POOLING_MAX = 0, /*!< Max pooling */
DL_POOLING_AVG = 1, /*!< Average pooling */
} dl_pooling_type;
/*
* Matrix for 3d
* @Warning: the sequence of variables is fixed, cannot be modified, otherwise there will be errors in esp_dsp_dot_float
*/
typedef struct
{
int w; /*!< Width */
int h; /*!< Height */
int c; /*!< Channel */
int n; /*!< Number of filter, input and output must be 1 */
int stride; /*!< Step between lines */
fptp_t *item; /*!< Data */
} dl_matrix3d_t;
typedef struct
{
int w; /*!< Width */
int h; /*!< Height */
int c; /*!< Channel */
int n; /*!< Number of filter, input and output must be 1 */
int stride; /*!< Step between lines */
uc_t *item; /*!< Data */
} dl_matrix3du_t;
typedef enum
{
UPSAMPLE_NEAREST_NEIGHBOR = 0, /*!< Use nearest neighbor interpolation as the upsample method*/
UPSAMPLE_BILINEAR = 1, /*!< Use nearest bilinear interpolation as the upsample method*/
} dl_upsample_type;
typedef struct
{
int stride_x; /*!< Strides of width */
int stride_y; /*!< Strides of height */
dl_padding_type padding; /*!< Padding type */
} dl_matrix3d_mobilenet_config_t;
/*
* @brief Allocate a zero-initialized space. Must use 'dl_lib_free' to free the memory.
*
* @param cnt Count of units.
* @param size Size of unit.
* @param align Align of memory. If not required, set 0.
* @return Pointer of allocated memory. Null for failed.
*/
static void *dl_lib_calloc(int cnt, int size, int align)
{
int total_size = cnt * size + align + sizeof(void *);
void *res = malloc(total_size);
if (NULL == res)
{
#if DL_SPIRAM_SUPPORT
res = heap_caps_malloc(total_size, MALLOC_CAP_8BIT | MALLOC_CAP_SPIRAM);
}
if (NULL == res)
{
printf("Item psram alloc failed. Size: %d x %d\n", cnt, size);
#else
printf("Item alloc failed. Size: %d x %d, SPIRAM_FLAG: %d\n", cnt, size, DL_SPIRAM_SUPPORT);
#endif
return NULL;
}
bzero(res, total_size);
void **data = (void **)res + 1;
void **aligned;
if (align)
aligned = (void **)(((size_t)data + (align - 1)) & -align);
else
aligned = data;
aligned[-1] = res;
return (void *)aligned;
}
/**
* @brief Free the memory space allocated by 'dl_lib_calloc'
*
*/
static inline void dl_lib_free(void *d)
{
if (NULL == d)
return;
free(((void **)d)[-1]);
}
/*
* @brief Allocate a 3D matrix with float items, the access sequence is NHWC
*
* @param n Number of matrix3d, for filters it is out channels, for others it is 1
* @param w Width of matrix3d
* @param h Height of matrix3d
* @param c Channel of matrix3d
* @return 3d matrix
*/
static inline dl_matrix3d_t *dl_matrix3d_alloc(int n, int w, int h, int c)
{
dl_matrix3d_t *r = (dl_matrix3d_t *)dl_lib_calloc(1, sizeof(dl_matrix3d_t), 0);
if (NULL == r)
{
printf("internal r failed.\n");
return NULL;
}
fptp_t *items = (fptp_t *)dl_lib_calloc(n * w * h * c, sizeof(fptp_t), 0);
if (NULL == items)
{
printf("matrix3d item alloc failed.\n");
dl_lib_free(r);
return NULL;
}
r->w = w;
r->h = h;
r->c = c;
r->n = n;
r->stride = w * c;
r->item = items;
return r;
}
/*
* @brief Allocate a 3D matrix with 8-bits items, the access sequence is NHWC
*
* @param n Number of matrix3d, for filters it is out channels, for others it is 1
* @param w Width of matrix3d
* @param h Height of matrix3d
* @param c Channel of matrix3d
* @return 3d matrix
*/
static inline dl_matrix3du_t *dl_matrix3du_alloc(int n, int w, int h, int c)
{
dl_matrix3du_t *r = (dl_matrix3du_t *)dl_lib_calloc(1, sizeof(dl_matrix3du_t), 0);
if (NULL == r)
{
printf("internal r failed.\n");
return NULL;
}
uc_t *items = (uc_t *)dl_lib_calloc(n * w * h * c, sizeof(uc_t), 0);
if (NULL == items)
{
printf("matrix3du item alloc failed.\n");
dl_lib_free(r);
return NULL;
}
r->w = w;
r->h = h;
r->c = c;
r->n = n;
r->stride = w * c;
r->item = items;
return r;
}
/*
* @brief Free a matrix3d
*
* @param m matrix3d with float items
*/
static inline void dl_matrix3d_free(dl_matrix3d_t *m)
{
if (NULL == m)
return;
if (NULL == m->item)
{
dl_lib_free(m);
return;
}
dl_lib_free(m->item);
dl_lib_free(m);
}
/*
* @brief Free a matrix3d
*
* @param m matrix3d with 8-bits items
*/
static inline void dl_matrix3du_free(dl_matrix3du_t *m)
{
if (NULL == m)
return;
if (NULL == m->item)
{
dl_lib_free(m);
return;
}
dl_lib_free(m->item);
dl_lib_free(m);
}
/*
* @brief Dot product with a vector and matrix
*
* @param out Space to put the result
* @param in input vector
* @param f filter matrix
*/
void dl_matrix3dff_dot_product(dl_matrix3d_t *out, dl_matrix3d_t *in, dl_matrix3d_t *f);
/**
* @brief Do a softmax operation on a matrix3d
*
* @param in Input matrix3d
*/
void dl_matrix3d_softmax(dl_matrix3d_t *m);
/**
* @brief Copy a range of float items from an existing matrix to a preallocated matrix
*
* @param dst The destination slice matrix
* @param src The source matrix to slice
* @param x X-offset of the origin of the returned matrix within the sliced matrix
* @param y Y-offset of the origin of the returned matrix within the sliced matrix
* @param w Width of the resulting matrix
* @param h Height of the resulting matrix
*/
void dl_matrix3d_slice_copy(dl_matrix3d_t *dst,
dl_matrix3d_t *src,
int x,
int y,
int w,
int h);
/**
* @brief Copy a range of 8-bits items from an existing matrix to a preallocated matrix
*
* @param dst The destination slice matrix
* @param src The source matrix to slice
* @param x X-offset of the origin of the returned matrix within the sliced matrix
* @param y Y-offset of the origin of the returned matrix within the sliced matrix
* @param w Width of the resulting matrix
* @param h Height of the resulting matrix
*/
void dl_matrix3du_slice_copy(dl_matrix3du_t *dst,
dl_matrix3du_t *src,
int x,
int y,
int w,
int h);
/**
* @brief Transform a sliced matrix block from nhwc to nchw, the block needs to be memory continous.
*
* @param out The destination sliced matrix in nchw
* @param in The source sliced matrix in nhwc
*/
void dl_matrix3d_sliced_transform_nchw(dl_matrix3d_t *out,
dl_matrix3d_t *in);
/**
* @brief Do a general CNN layer pass, dimension is (number, width, height, channel)
*
* @param in Input matrix3d
* @param filter Weights of the neurons
* @param bias Bias for the CNN layer
* @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 dl_matrix3d_t* The result of CNN layer
*/
dl_matrix3d_t *dl_matrix3d_conv(dl_matrix3d_t *in,
dl_matrix3d_t *filter,
dl_matrix3d_t *bias,
int stride_x,
int stride_y,
int padding,
int mode);
/**
* @brief Do a global average pooling layer pass, dimension is (number, width, height, channel)
*
* @param in Input matrix3d
*
* @return The result of global average pooling layer
*/
dl_matrix3d_t *dl_matrix3d_global_pool(dl_matrix3d_t *in);
/**
* @brief Calculate pooling layer of a feature map
*
* @param in Input matrix, size (1, w, h, c)
* @param f_w Window width
* @param f_h Window height
* @param stride_x Stride in horizontal direction
* @param stride_y Stride in vertical direction
* @param padding Padding type: PADDING_VALID and PADDING_SAME
* @param pooling_type Pooling type: DL_POOLING_MAX and POOLING_AVG
* @return dl_matrix3d_t* Resulting matrix, size (1, w', h', c)
*/
dl_matrix3d_t *dl_matrix3d_pooling(dl_matrix3d_t *in,
int f_w,
int f_h,
int stride_x,
int stride_y,
dl_padding_type padding,
dl_pooling_type pooling_type);
/**
* @brief Do a batch normalization operation, update the input matrix3d: input = input * scale + offset
*
* @param m Input matrix3d
* @param scale scale matrix3d, scale = gamma/((moving_variance+sigma)^(1/2))
* @param Offset Offset matrix3d, offset = beta-(moving_mean*gamma/((moving_variance+sigma)^(1/2)))
*/
void dl_matrix3d_batch_normalize(dl_matrix3d_t *m,
dl_matrix3d_t *scale,
dl_matrix3d_t *offset);
/**
* @brief Add a pair of matrix3d item-by-item: res=in_1+in_2
*
* @param in_1 First Floating point input matrix3d
* @param in_2 Second Floating point input matrix3d
*
* @return dl_matrix3d_t* Added data
*/
dl_matrix3d_t *dl_matrix3d_add(dl_matrix3d_t *in_1, dl_matrix3d_t *in_2);
/**
* @brief Concatenate the channels of two matrix3ds into a new matrix3d
*
* @param in_1 First Floating point input matrix3d
* @param in_2 Second Floating point input matrix3d
*
* @return dl_matrix3d_t* A newly allocated matrix3d with as avlues in_1|in_2
*/
dl_matrix3d_t *dl_matrix3d_concat(dl_matrix3d_t *in_1, dl_matrix3d_t *in_2);
/**
* @brief Concatenate the channels of four matrix3ds into a new matrix3d
*
* @param in_1 First Floating point input matrix3d
* @param in_2 Second Floating point input matrix3d
* @param in_3 Third Floating point input matrix3d
* @param in_4 Fourth Floating point input matrix3d
*
* @return A newly allocated matrix3d with as avlues in_1|in_2|in_3|in_4
*/
dl_matrix3d_t *dl_matrix3d_concat_4(dl_matrix3d_t *in_1,
dl_matrix3d_t *in_2,
dl_matrix3d_t *in_3,
dl_matrix3d_t *in_4);
/**
* @brief Concatenate the channels of eight matrix3ds into a new matrix3d
*
* @param in_1 First Floating point input matrix3d
* @param in_2 Second Floating point input matrix3d
* @param in_3 Third Floating point input matrix3d
* @param in_4 Fourth Floating point input matrix3d
* @param in_5 Fifth Floating point input matrix3d
* @param in_6 Sixth Floating point input matrix3d
* @param in_7 Seventh Floating point input matrix3d
* @param in_8 eighth Floating point input matrix3d
*
* @return A newly allocated matrix3d with as avlues in_1|in_2|in_3|in_4|in_5|in_6|in_7|in_8
*/
dl_matrix3d_t *dl_matrix3d_concat_8(dl_matrix3d_t *in_1,
dl_matrix3d_t *in_2,
dl_matrix3d_t *in_3,
dl_matrix3d_t *in_4,
dl_matrix3d_t *in_5,
dl_matrix3d_t *in_6,
dl_matrix3d_t *in_7,
dl_matrix3d_t *in_8);
/**
* @brief Do a mobilefacenet block forward, dimension is (number, width, height, channel)
*
* @param in Input matrix3d
* @param pw Weights of the pointwise conv layer
* @param pw_bn_scale The scale params of the batch_normalize layer after the pointwise conv layer
* @param pw_bn_offset The offset params of the batch_normalize layer after the pointwise conv layer
* @param dw Weights of the depthwise conv layer
* @param dw_bn_scale The scale params of the batch_normalize layer after the depthwise conv layer
* @param dw_bn_offset The offset params of the batch_normalize layer after the depthwise conv layer
* @param pw_linear Weights of the pointwise linear conv layer
* @param pw_linear_bn_scale The scale params of the batch_normalize layer after the pointwise linear conv layer
* @param pw_linear_bn_offset The offset params of the batch_normalize layer after the pointwise linear conv layer
* @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 a mobilefacenet block
*/
dl_matrix3d_t *dl_matrix3d_mobilefaceblock(dl_matrix3d_t *in,
dl_matrix3d_t *pw,
dl_matrix3d_t *pw_bn_scale,
dl_matrix3d_t *pw_bn_offset,
dl_matrix3d_t *dw,
dl_matrix3d_t *dw_bn_scale,
dl_matrix3d_t *dw_bn_offset,
dl_matrix3d_t *pw_linear,
dl_matrix3d_t *pw_linear_bn_scale,
dl_matrix3d_t *pw_linear_bn_offset,
int stride_x,
int stride_y,
int padding,
int mode,
int shortcut);
/**
* @brief Do a mobilefacenet block forward with 1x1 split conv, dimension is (number, width, height, channel)
*
* @param in Input matrix3d
* @param pw_1 Weights of the pointwise conv layer 1
* @param pw_2 Weights of the pointwise conv layer 2
* @param pw_bn_scale The scale params of the batch_normalize layer after the pointwise conv layer
* @param pw_bn_offset The offset params of the batch_normalize layer after the pointwise conv layer
* @param dw Weights of the depthwise conv layer
* @param dw_bn_scale The scale params of the batch_normalize layer after the depthwise conv layer
* @param dw_bn_offset The offset params of the batch_normalize layer after the depthwise conv layer
* @param pw_linear_1 Weights of the pointwise linear conv layer 1
* @param pw_linear_2 Weights of the pointwise linear conv layer 2
* @param pw_linear_bn_scale The scale params of the batch_normalize layer after the pointwise linear conv layer
* @param pw_linear_bn_offset The offset params of the batch_normalize layer after the pointwise linear conv layer
* @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 a mobilefacenet block
*/
dl_matrix3d_t *dl_matrix3d_mobilefaceblock_split(dl_matrix3d_t *in,
dl_matrix3d_t *pw_1,
dl_matrix3d_t *pw_2,
dl_matrix3d_t *pw_bn_scale,
dl_matrix3d_t *pw_bn_offset,
dl_matrix3d_t *dw,
dl_matrix3d_t *dw_bn_scale,
dl_matrix3d_t *dw_bn_offset,
dl_matrix3d_t *pw_linear_1,
dl_matrix3d_t *pw_linear_2,
dl_matrix3d_t *pw_linear_bn_scale,
dl_matrix3d_t *pw_linear_bn_offset,
int stride_x,
int stride_y,
int padding,
int mode,
int shortcut);
/**
* @brief Initialize the matrix3d feature map to bias
*
* @param out The matrix3d feature map needs to be initialized
* @param bias The bias of a convlotion operation
*/
void dl_matrix3d_init_bias(dl_matrix3d_t *out, dl_matrix3d_t *bias);
/**
* @brief Do a elementwise multiplication of two matrix3ds
*
* @param out Preallocated matrix3d, size (n, w, h, c)
* @param in1 Input matrix 1, size (n, w, h, c)
* @param in2 Input matrix 2, size (n, w, h, c)
*/
void dl_matrix3d_multiply(dl_matrix3d_t *out, dl_matrix3d_t *in1, dl_matrix3d_t *in2);
//
// Activation
//
/**
* @brief Do a standard relu operation, update the input matrix3d
*
* @param m Floating point input matrix3d
*/
void dl_matrix3d_relu(dl_matrix3d_t *m);
/**
* @brief Do a relu (Rectifier Linear Unit) operation, update the input matrix3d
*
* @param in Floating point input matrix3d
* @param clip If value is higher than this, it will be clipped to this value
*/
void dl_matrix3d_relu_clip(dl_matrix3d_t *m, fptp_t clip);
/**
* @brief Do a Prelu (Rectifier Linear Unit) operation, update the input matrix3d
*
* @param in Floating point input matrix3d
* @param alpha If value is less than zero, it will be updated by multiplying this factor
*/
void dl_matrix3d_p_relu(dl_matrix3d_t *in, dl_matrix3d_t *alpha);
/**
* @brief Do a leaky relu (Rectifier Linear Unit) operation, update the input matrix3d
*
* @param in Floating point input matrix3d
* @param alpha If value is less than zero, it will be updated by multiplying this factor
*/
void dl_matrix3d_leaky_relu(dl_matrix3d_t *m, fptp_t alpha);
//
// Conv 1x1
//
/**
* @brief Do 1x1 convolution with a matrix3d
*
* @param out Preallocated matrix3d, size (1, w, h, n)
* @param in Input matrix, size (1, w, h, c)
* @param filter 1x1 filter, size (n, 1, 1, c)
*/
void dl_matrix3dff_conv_1x1(dl_matrix3d_t *out,
dl_matrix3d_t *in,
dl_matrix3d_t *filter);
/**
* @brief Do 1x1 convolution with a matrix3d, with bias adding
*
* @param out Preallocated matrix3d, size (1, w, h, n)
* @param in Input matrix, size (1, w, h, c)
* @param filter 1x1 filter, size (n, 1, 1, c)
* @param bias Bias, size (1, 1, 1, n)
*/
void dl_matrix3dff_conv_1x1_with_bias(dl_matrix3d_t *out,
dl_matrix3d_t *in,
dl_matrix3d_t *filter,
dl_matrix3d_t *bias);
/**
* @brief Do 1x1 convolution with an 8-bit fixed point matrix
*
* @param out Preallocated matrix3d, size (1, w, h, n)
* @param in Input matrix, size (1, w, h, c)
* @param filter 1x1 filter, size (n, 1, 1, c)
*/
void dl_matrix3duf_conv_1x1(dl_matrix3d_t *out,
dl_matrix3du_t *in,
dl_matrix3d_t *filter);
/**
* @brief Do 1x1 convolution with an 8-bit fixed point matrix, with bias adding
*
* @param out Preallocated matrix3d, size (1, w, h, n)
* @param in Input matrix, size (1, w, h, c)
* @param filter 1x1 filter, size (n, 1, 1, c)
* @param bias Bias, size (1, 1, 1, n)
*/
void dl_matrix3duf_conv_1x1_with_bias(dl_matrix3d_t *out,
dl_matrix3du_t *in,
dl_matrix3d_t *filter,
dl_matrix3d_t *bias);
//
// Conv 3x3
//
/**
* @brief Do 3x3 convolution with a matrix3d, without padding
*
* @param out Preallocated matrix3d, size (1, w, h, n)
* @param in Input matrix, size (1, w, h, c)
* @param f 3x3 filter, size (n, 3, 3, c)
* @param step_x Stride of width
* @param step_y Stride of height
*/
void dl_matrix3dff_conv_3x3_op(dl_matrix3d_t *out,
dl_matrix3d_t *in,
dl_matrix3d_t *f,
int step_x,
int step_y);
/**
* @brief Do 3x3 convolution with a matrix3d, with bias adding
*
* @param input Input matrix, size (1, w, h, c)
* @param filter 3x3 filter, size (n, 3, 3, c)
* @param bias Bias, size (1, 1, 1, n)
* @param stride_x Stride of width
* @param stride_y Stride of height
* @param padding Padding type
* @return dl_matrix3d_t* Resulting matrix3d
*/
dl_matrix3d_t *dl_matrix3dff_conv_3x3(dl_matrix3d_t *in,
dl_matrix3d_t *filter,
dl_matrix3d_t *bias,
int stride_x,
int stride_y,
dl_padding_type padding);
//
// Conv Common
//
/**
* @brief Do a general convolution layer pass with an 8-bit fixed point matrix, size is (number, width, height, channel)
*
* @param in Input image
* @param filter Weights of the neurons
* @param bias Bias for the CNN layer
* @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 Padding type
* @return dl_matrix3d_t* Resulting matrix3d
*/
dl_matrix3d_t *dl_matrix3duf_conv_common(dl_matrix3du_t *in,
dl_matrix3d_t *filter,
dl_matrix3d_t *bias,
int stride_x,
int stride_y,
dl_padding_type padding);
/**
* @brief Do a general convolution layer pass, size is (number, width, height, channel)
*
* @param in Input image
* @param filter Weights of the neurons
* @param bias Bias for the CNN layer
* @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 Padding type
* @return dl_matrix3d_t* Resulting matrix3d
*/
dl_matrix3d_t *dl_matrix3dff_conv_common(dl_matrix3d_t *in,
dl_matrix3d_t *filter,
dl_matrix3d_t *bias,
int stride_x,
int stride_y,
dl_padding_type padding);
//
// Depthwise 3x3
//
/**
* @brief Do 3x3 depthwise convolution with a float matrix3d
*
* @param in Input matrix, size (1, w, h, c)
* @param filter 3x3 filter, size (1, 3, 3, c)
* @param stride_x Stride of width
* @param stride_y Stride of height
* @param padding Padding type, 0: valid, 1: same
* @return dl_matrix3d_t* Resulting float matrix3d
*/
dl_matrix3d_t *dl_matrix3dff_depthwise_conv_3x3(dl_matrix3d_t *in,
dl_matrix3d_t *filter,
int stride_x,
int stride_y,
int padding);
/**
* @brief Do 3x3 depthwise convolution with a 8-bit fixed point matrix
*
* @param in Input matrix, size (1, w, h, c)
* @param filter 3x3 filter, size (1, 3, 3, c)
* @param stride_x Stride of width
* @param stride_y Stride of height
* @param padding Padding type, 0: valid, 1: same
* @return dl_matrix3d_t* Resulting float matrix3d
*/
dl_matrix3d_t *dl_matrix3duf_depthwise_conv_3x3(dl_matrix3du_t *in,
dl_matrix3d_t *filter,
int stride_x,
int stride_y,
int padding);
/**
* @brief Do 3x3 depthwise convolution with a float matrix3d, without padding
*
* @param out Preallocated matrix3d, size (1, w, h, n)
* @param in Input matrix, size (1, w, h, c)
* @param f 3x3 filter, size (1, 3, 3, c)
* @param step_x Stride of width
* @param step_y Stride of height
*/
void dl_matrix3dff_depthwise_conv_3x3_op(dl_matrix3d_t *out,
dl_matrix3d_t *in,
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
* @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);
/**
* @brief Do fully connected layer forward, with bias adding
*
* @param out Preallocated resulting matrix, size (1, 1, 1, h)
* @param in Input matrix, size (1, 1, 1, w)
* @param filter Filter matrix, size (1, w, h, 1)
* @param bias Bias matrix, size (1, 1, 1, h)
*/
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);