#pragma once
typedef float fptp_t;
typedef uint8_t uc_t;
typedef enum
{
DL_C_IMPL = 0,
DL_XTENSA_IMPL = 1
} dl_conv_mode;
typedef enum
{
INPUT_UINT8 = 0,
INPUT_FLOAT = 1,
} dl_op_type;
typedef enum
{
PADDING_VALID = 0,
PADDING_SAME = 1,
} dl_padding_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
{
/******* fix start *******/
int w; // Width
int h; // Height
int c; // Channel
int n; // Number, to record filter's out_channels. input and output must be 1
int stride;
fptp_t *item;
/******* fix end *******/
} dl_matrix3d_t;
typedef struct
{
int w; // Width
int h; // Height
int c; // Channel
int n; // Number, to record filter's out_channels. input and output must be 1
int stride;
uc_t *item;
} dl_matrix3du_t;
typedef struct
{
int stride_x;
int stride_y;
dl_padding_type padding;
dl_conv_mode mode;
dl_op_type type;
} dl_matrix3d_conv_config_t;
/*
* @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
*/
dl_matrix3d_t *dl_matrix3d_alloc(int n, int w, int h, int c);
/*
* @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
*/
dl_matrix3du_t *dl_matrix3du_alloc(int n, int w, int h, int c);
/*
* @brief Free a matrix3d
*
* @param m matrix3d with float items
*/
void dl_matrix3d_free(dl_matrix3d_t *m);
/*
* @brief Free a matrix3d
*
* @param m matrix3d with 8-bits items
*/
void dl_matrix3du_free(dl_matrix3du_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(dl_matrix3d_t *m, fptp_t clip);
/**
* @brief Do a leaky 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
* @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 clip, fptp_t alpha);
/**
* @brief Do a softmax operation on a matrix3d
*
* @param in Input matrix3d
*/
void dl_matrix3d_softmax(dl_matrix3d_t *m);
/**
* @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)
*/
dl_matrix3d_t *dl_matrix3d_fc(dl_matrix3d_t *in,
dl_matrix3d_t *filter,
dl_matrix3d_t *bias);
/**
* @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 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 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 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 The result of CNN layer
*/
dl_matrix3d_t *dl_matrix3du_conv(dl_matrix3du_t *in,
dl_matrix3d_t *filter,
dl_matrix3d_t *bias,
int stride_x,
int stride_y,
int padding,
int mode);
/**
* @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_matrix3d_depthwise_conv(dl_matrix3d_t *in,
dl_matrix3d_t *filter,
int stride_x,
int stride_y,
int padding,
int mode);
/**
* @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_matrix3d_mobilenet(void *in,
dl_matrix3d_t *dilate,
dl_matrix3d_t *depthwise,
dl_matrix3d_t *compress,
dl_matrix3d_t *bias,
dl_matrix3d_t *prelu,
dl_matrix3d_conv_config_t *config);
/**
* @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 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 Added data
*/
dl_matrix3d_t *dl_matrix3d_add(dl_matrix3d_t *in_1, dl_matrix3d_t *in_2);
/**
* @brief Do a standard relu operation, update the input matrix3d
*
* @param m Floating point input matrix3d
*/
void dl_matrix3d_relu_std(dl_matrix3d_t *m);
/**
* @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 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(void *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(void *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 Print the matrix3d items
*
* @param m dl_matrix3d_t to be printed
* @param message name of matrix
*/
void dl_matrix3d_print(dl_matrix3d_t *m, char *message);
/**
* @brief Print the matrix3du items
*
* @param m dl_matrix3du_t to be printed
* @param message name of matrix
*/
void dl_matrix3du_print(dl_matrix3du_t *m, char *message);