1041 lines
45 KiB
C
1041 lines
45 KiB
C
#pragma once
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#include "dl_lib_matrix3d.h"
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typedef int16_t qtp_t;
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/**
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* Matrix for input, filter, and output
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* @Warning: the sequence of variables is fixed, cannot be modified, otherwise there will be errors in
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* some handwrite xtensa instruction functions
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*/
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typedef struct
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{
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/******* fix start *******/
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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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int exponent; /*!< Exponent for quantization */
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qtp_t *item; /*!< Data */
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/******* fix end *******/
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} dl_matrix3dq_t;
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#ifndef DL_QTP_SHIFT
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#define DL_QTP_SHIFT 15
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#define DL_ITMQ(m, x, y) m->itemq[(y) + (x)*m->stride]
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#define DL_QTP_RANGE ((1 << DL_QTP_SHIFT) - 1)
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#define DL_QTP_MAX 32767
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#define DL_QTP_MIN -32768
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#define DL_QTP_EXP_NA 255 //non-applicable exponent because matrix is null
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#define DL_SHIFT_AUTO 32
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#endif
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/**
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* Implementation of matrix relative operations
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*/
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typedef enum
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{
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DL_C_IMPL = 0, /*!< ANSI C */
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DL_XTENSA_IMPL = 1 /*!< Handwrite xtensa instruction */
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} dl_conv_mode;
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/**
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* Configuration of mobilenet operation
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*/
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typedef struct
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{
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int stride_x; /*!< Strides of width */
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int stride_y; /*!< Strides of height */
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dl_padding_type padding; /*!< Padding type */
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dl_conv_mode mode; /*!< Implementation mode */
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int dilate_exponent; /*!< Exponent of dilation filter */
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int depthwise_exponent; /*!< Exponent of depthwise filter */
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int compress_exponent; /*!< Exponent of compress filter */
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} dl_matrix3dq_mobilenet_config_t;
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//
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// Utility
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//
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/*
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* @brief Allocate a 3d quantised matrix
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*
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* @param n Number of filters, for input and output, should be 1
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* @param w Width of matrix
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* @param h Height of matrix
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* @param c Channel of matrix
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* @param e Exponent of matrix data
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* @return 3d quantized matrix
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*/
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static inline dl_matrix3dq_t *dl_matrix3dq_alloc(int n, int w, int h, int c, int e)
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{
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dl_matrix3dq_t *r = (dl_matrix3dq_t *)dl_lib_calloc(1, sizeof(dl_matrix3dq_t), 0);
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if (NULL == r)
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{
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printf("dl_matrix3dq alloc failed.\n");
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return NULL;
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}
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qtp_t *items = (qtp_t *)dl_lib_calloc(n * w * h * c, sizeof(qtp_t), 16);
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if (NULL == items)
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{
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printf("matrix3dq 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->exponent = e;
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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 3d quantized matrix
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*
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* @param m 3d quantised matrix
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*/
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static inline void dl_matrix3dq_free(dl_matrix3dq_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 Copy a range of items from an existing matrix to a preallocated matrix
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*
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* @param dst The resulting slice matrix
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* @param src Old 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_matrix3dq_slice_copy(dl_matrix3dq_t *dst, dl_matrix3dq_t *src, int x, int y, int w, 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_matrix3dq_sliced_transform_nchw(dl_matrix3dq_t *out,
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dl_matrix3dq_t *in);
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/**
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* @brief Transform a fixed point matrix to a float point matrix
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*
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* @param m Quantized matrix
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* @return Float point matrix
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*/
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dl_matrix3d_t *dl_matrix3d_from_matrixq(dl_matrix3dq_t *m);
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/**
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* @brief Transform a float point matrix to a fixed point matrix with pre-defined exponent
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*
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* @param m Float point matrix
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* @param exponent Exponent for resulting matrix
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* @return Fixed point matrix
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*/
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dl_matrix3dq_t *dl_matrixq_from_matrix3d_qmf(dl_matrix3d_t *m, int exponent);
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/**
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* @brief Transform a float point matrix to a fixed point matrix. The exponent is defined by the distribution of the input matrix.
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*
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* @param m Float point matrix
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* @return Fixed point matrix
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*/
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dl_matrix3dq_t *dl_matrixq_from_matrix3d(dl_matrix3d_t *m);
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qtp_t dl_matrix3dq_quant_range_exceeded_checking(int64_t value, char *location);
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/**
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* @brief Reform a quantized matrix with exponent
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*
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* @param out Preallocated resulting matrix
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* @param in Input matrix
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* @param exponent Exponent for resulting matrix
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*/
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void dl_matrix3dq_shift_exponent(dl_matrix3dq_t *out, dl_matrix3dq_t *in, int exponent);
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/**
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* @brief Do batch normalization for a quantized matrix
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*
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* @param m Input and output quantized matrix, data will be updated
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* @param scale Scale of batch-norm
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* @param offset Offset of batch-norm
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*/
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void dl_matrix3dq_batch_normalize(dl_matrix3dq_t *m, dl_matrix3dq_t *scale, dl_matrix3dq_t *offset);
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/**
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* @brief Add two quantized matrix with a pre-defined exponent
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*
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* @param in_1 Adder 1
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* @param in_2 Adder 2
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* @param exponent Exponent for resulting matrix
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* @return Result of accumulation of two matrix
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*/
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dl_matrix3dq_t *dl_matrix3dq_add(dl_matrix3dq_t *in_1, dl_matrix3dq_t *in_2, int exponent);
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//
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// Activation
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//
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/**
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* @brief Do relu for a quantized matrix
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*
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* @param in Input and output quantized matrix, data will be updated
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*/
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void dl_matrix3dq_relu(dl_matrix3dq_t *in);
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/**
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* @brief Do relu with clips for a quantized matrix
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*
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* @param in Input and output quantized matrix, data will be updated
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* @param clip Float point value to limit the maximum data
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*/
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void dl_matrix3dq_relu_clip(dl_matrix3dq_t *in, fptp_t clip);
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/**
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* @brief Do leaky relu for a quantized matrix
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*
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* @param in Input and output quantized matrix, data will be updated
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* @param alpha Float point value to multiply for those less than zero
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* @param clip Float point value to limit the maximum data
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*/
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void dl_matrix3dq_leaky_relu(dl_matrix3dq_t *in, fptp_t alpha, fptp_t clip);
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/**
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* @brief Do prelu for a quantized matrix
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*
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* @param in Input and output quantized matrix, data will be updated
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* @param alpha Quantized matrix to multiply for those less than zero
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*/
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void dl_matrix3dq_p_relu(dl_matrix3dq_t *in, dl_matrix3dq_t *alpha);
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//
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// Concat
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//
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/**
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* @brief Concatenate two quantized matrix in channel
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*
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* @param in_1 Quantized matrix to be concatenated
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* @param in_2 Quantized matrix to be concatenated
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* @return Quantized matrix with the same width and height of in_1 and in_2, and with the sum of channel number of in_1 and in_2
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*/
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dl_matrix3dq_t *dl_matrix3dq_concat(dl_matrix3dq_t *in_1,
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dl_matrix3dq_t *in_2);
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/**
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* @brief Concatenate four quantized matrix in channel
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*
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* @param in_1 Quantized matrix to be concatenated
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* @param in_2 Quantized matrix to be concatenated
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* @param in_3 Quantized matrix to be concatenated
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* @param in_4 Quantized matrix to be concatenated
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* @return Quantized matrix with the same width and height of all inputs, and with the sum of channel number of all inputs
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*/
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dl_matrix3dq_t *dl_matrix3dq_concat_4(dl_matrix3dq_t *in_1,
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dl_matrix3dq_t *in_2,
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dl_matrix3dq_t *in_3,
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dl_matrix3dq_t *in_4);
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/**
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* @brief Concatenate four quantized matrix in channel
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*
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* @param in_1 Quantized matrix to be concatenated
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* @param in_2 Quantized matrix to be concatenated
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* @param in_3 Quantized matrix to be concatenated
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* @param in_4 Quantized matrix to be concatenated
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* @param in_5 Quantized matrix to be concatenated
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* @param in_6 Quantized matrix to be concatenated
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* @param in_7 Quantized matrix to be concatenated
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* @param in_8 Quantized matrix to be concatenated
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* @return Quantized matrix with the same width and height of all inputs, and with the sum of channel number of all inputs
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*/
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dl_matrix3dq_t *dl_matrix3dq_concat_8(dl_matrix3dq_t *in_1,
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dl_matrix3dq_t *in_2,
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dl_matrix3dq_t *in_3,
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dl_matrix3dq_t *in_4,
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dl_matrix3dq_t *in_5,
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dl_matrix3dq_t *in_6,
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dl_matrix3dq_t *in_7,
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dl_matrix3dq_t *in_8);
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//
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// Conv 1x1
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//
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/**
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* @brief Do 1x1 convolution with a quantized matrix
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*
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* @param out Preallocated quantized matrix, size (1, w, h, n)
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* @param in Input matrix, size (1, w, h, c)
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* @param filter 1x1 filter, size (n, 1, 1, c)
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* @param mode Implementation mode
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*/
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void dl_matrix3dqq_conv_1x1(dl_matrix3dq_t *out,
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dl_matrix3dq_t *in,
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dl_matrix3dq_t *filter,
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dl_conv_mode mode,
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char *name);
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/**
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* @brief Do 1x1 convolution with a quantized matrix, with relu activation
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*
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* @param out Preallocated quantized matrix, size (1, w, h, n)
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* @param in Input matrix, size (1, w, h, c)
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* @param filter 1x1 filter, size (n, 1, 1, c)
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* @param mode Implementation mode
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*/
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void dl_matrix3dqq_conv_1x1_with_relu(dl_matrix3dq_t *out,
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dl_matrix3dq_t *in,
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dl_matrix3dq_t *filter,
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dl_conv_mode mode,
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char *name);
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/**
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* @brief Do 1x1 convolution with a quantized matrix, with bias adding
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*
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* @param out Preallocated quantized matrix, size (1, w, h, n)
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* @param in Input matrix, size (1, w, h, c)
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* @param filter 1x1 filter, size (n, 1, 1, c)
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* @param bias Bias, size (1, 1, 1, n)
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* @param mode Implementation mode
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* @param name Layer name to debug
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*/
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void dl_matrix3dqq_conv_1x1_with_bias(dl_matrix3dq_t *out,
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dl_matrix3dq_t *in,
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dl_matrix3dq_t *filter,
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dl_matrix3dq_t *bias,
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dl_conv_mode mode,
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char *name);
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/**
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* @brief Do 1x1 convolution with a quantized matrix, with bias adding and relu activation
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*
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* @param out Preallocated quantized matrix, size (1, w, h, n)
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* @param in Input matrix, size (1, w, h, c)
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* @param filter 1x1 filter, size (n, 1, 1, c)
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* @param bias Bias, size (1, 1, 1, n)
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* @param mode Implementation mode
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*/
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void dl_matrix3dqq_conv_1x1_with_bias_relu(dl_matrix3dq_t *out,
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dl_matrix3dq_t *in,
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dl_matrix3dq_t *filter,
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dl_matrix3dq_t *bias,
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dl_conv_mode mode,
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char *name);
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/**
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* @brief
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*
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* @param out
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* @param in
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* @param filter
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* @param prelu
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* @param mode
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* @param name
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*/
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void dl_matrix3dqq_conv_1x1_with_prelu(dl_matrix3dq_t *out,
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dl_matrix3dq_t *in,
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dl_matrix3dq_t *filter,
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dl_matrix3dq_t *prelu,
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dl_conv_mode mode,
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char *name);
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/**
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* @brief Do 1x1 convolution with an 8-bit fixed point matrix
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*
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* @param out Preallocated quantized matrix, size (1, w, h, n)
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* @param in Input matrix, size (1, w, h, c)
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* @param filter 1x1 filter, size (n, 1, 1, c)
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* @param mode Implementation mode
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*/
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void dl_matrix3duq_conv_1x1(dl_matrix3dq_t *out,
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dl_matrix3du_t *in,
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dl_matrix3dq_t *filter,
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dl_conv_mode mode,
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char *name);
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/**
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* @brief Do 1x1 convolution with an 8-bit fixed point matrix, with bias adding
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*
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* @param out Preallocated quantized matrix, size (1, w, h, n)
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* @param in Input matrix, size (1, w, h, c)
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* @param filter 1x1 filter, size (n, 1, 1, c)
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* @param bias Bias, size (1, 1, 1, n)
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* @param mode Implementation mode
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*/
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void dl_matrix3duq_conv_1x1_with_bias(dl_matrix3dq_t *out,
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dl_matrix3du_t *in,
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dl_matrix3dq_t *filter,
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dl_matrix3dq_t *bias,
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dl_conv_mode mode,
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char *name);
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//
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// Conv 3x3
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//
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/**
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* @brief Do 3x3 convolution with a quantized matrix
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*
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* @param input Input matrix, size (1, w, h, c)
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* @param filter 3x3 filter, size (n, 3, 3, c)
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* @param stride_x Stride of width
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* @param stride_y Stride of height
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* @param padding Padding type, 0: valid, 1: same
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* @param exponent Exponent for resulting matrix
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* @param name
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* @return dl_matrix3dq_t* Resulting quantized matrix
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*/
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dl_matrix3dq_t *dl_matrix3dqq_conv_3x3(dl_matrix3dq_t *input,
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dl_matrix3dq_t *filter,
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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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int exponent,
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char *name);
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/**
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* @brief Do 3x3 convolution with a quantized matrix, with bias adding
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*
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* @param input Input matrix, size (1, w, h, c)
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* @param filter 3x3 filter, size (n, 3, 3, c)
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* @param bias Bias, size (1, 1, 1, n)
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* @param stride_x Stride of width
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* @param stride_y Stride of height
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* @param padding
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* @param exponent Exponent for resulting matrix
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* @param name
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* @return dl_matrix3dq_t* Resulting quantized matrix
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*/
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dl_matrix3dq_t *dl_matrix3dqq_conv_3x3_with_bias(dl_matrix3dq_t *input,
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dl_matrix3dq_t *filter,
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dl_matrix3dq_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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int exponent,
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char *name);
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/**
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* @brief Do 3x3 convolution with a quantized matrix, with bias adding, relu activation
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*
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* @param input Input matrix, size (1, w, h, c)
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* @param filter 3x3 filter, size (n, 3, 3, c)
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* @param bias Bias, size (1, 1, 1, n)
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* @param stride_x Stride of width
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* @param stride_y Stride of height
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* @param padding
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* @param exponent Exponent for resulting matrix
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* @param name
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* @return dl_matrix3dq_t* Resulting quantized matrix
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*/
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dl_matrix3dq_t *dl_matrix3dqq_conv_3x3_with_bias_relu(dl_matrix3dq_t *input,
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dl_matrix3dq_t *filter,
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dl_matrix3dq_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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int exponent,
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char *name);
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/**
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* @brief
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*
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* @param input
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* @param filter
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* @param bias
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* @param stride_x
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* @param stride_y
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* @param padding
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* @param exponent
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* @param name
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* @return dl_matrix3dq_t*
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*/
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dl_matrix3dq_t *dl_matrix3duq_conv_3x3_with_bias(dl_matrix3du_t *input,
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dl_matrix3dq_t *filter,
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dl_matrix3dq_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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int exponent,
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char *name);
|
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|
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/**
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* @brief
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*
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* @param input
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* @param filter
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* @param bias
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* @param prelu
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* @param stride_x
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* @param stride_y
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* @param padding
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* @param exponent
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* @param name
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* @return dl_matrix3dq_t*
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*/
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dl_matrix3dq_t *dl_matrix3duq_conv_3x3_with_bias_prelu(dl_matrix3du_t *input,
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dl_matrix3dq_t *filter,
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dl_matrix3dq_t *bias,
|
|
dl_matrix3dq_t *prelu,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
int exponent,
|
|
char *name);
|
|
|
|
|
|
|
|
dl_matrix3dq_t *dl_matrix3dqq_conv_3x3_with_bias_prelu(dl_matrix3dq_t *input,
|
|
dl_matrix3dq_t *filter,
|
|
dl_matrix3dq_t *bias,
|
|
dl_matrix3dq_t *prelu,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
int exponent,
|
|
char *name);
|
|
//
|
|
// Conv common
|
|
//
|
|
|
|
/**
|
|
* @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 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.
|
|
* @return The result of CNN layer.
|
|
*/
|
|
dl_matrix3dq_t *dl_matrix3dqq_conv_common(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *filter,
|
|
dl_matrix3dq_t *bias,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
int exponent,
|
|
dl_conv_mode mode);
|
|
|
|
/**
|
|
* @brief Do a general convolution layer pass for 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 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.
|
|
* @return The result of CNN layer.
|
|
*/
|
|
dl_matrix3dq_t *dl_matrix3duq_conv_common(dl_matrix3du_t *in,
|
|
dl_matrix3dq_t *filter,
|
|
dl_matrix3dq_t *bias,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
int exponent,
|
|
dl_conv_mode mode);
|
|
|
|
//
|
|
// Depthwise 3x3
|
|
//
|
|
/**
|
|
* @brief Do 3x3 depthwise convolution with an 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
|
|
* @param exponent Exponent for resulting matrix
|
|
* @return Resulting quantized matrix
|
|
*/
|
|
dl_matrix3dq_t *dl_matrix3duq_depthwise_conv_3x3(dl_matrix3du_t *in,
|
|
dl_matrix3dq_t *filter,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
int exponent,
|
|
char *name);
|
|
|
|
/**
|
|
* @brief Do 3x3 depthwise convolution with a quantized 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
|
|
* @param exponent Exponent for resulting matrix
|
|
* @return Resulting quantized matrix
|
|
*/
|
|
dl_matrix3dq_t *dl_matrix3dqq_depthwise_conv_3x3(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *filter,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
int exponent,
|
|
char *name);
|
|
|
|
#if CONFIG_DEVELOPING_CODE
|
|
dl_matrix3dq_t *dl_matrix3dqq_depthwise_conv_3x3_2(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *filter,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
int exponent,
|
|
char *name);
|
|
|
|
dl_matrix3dq_t *dl_matrix3dqq_depthwise_conv_3x3_3(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *filter,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
int exponent,
|
|
char *name);
|
|
#endif
|
|
|
|
/**
|
|
* @brief Do 3x3 depthwise convolution with a quantized matrix, with bias adding
|
|
*
|
|
* @param in Input matrix, size (1, w, h, c)
|
|
* @param filter 3x3 filter, size (1, 3, 3, c)
|
|
* @param bias Bias, size (1, 1, 1, c)
|
|
* @param stride_x Stride of width
|
|
* @param stride_y Stride of height
|
|
* @param padding Padding type, 0: valid, 1: same
|
|
* @param exponent Exponent for resulting matrix
|
|
* @param relu Whether to use relu activation
|
|
* @return Resulting quantized matrix
|
|
*/
|
|
dl_matrix3dq_t *dl_matrix3dqq_depthwise_conv_3x3_with_bias(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *f,
|
|
dl_matrix3dq_t *bias,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
int exponent,
|
|
int relu,
|
|
char *name);
|
|
|
|
/**
|
|
* @brief Do 3x3 depthwise convolution with a quantized matrix, with bias adding and stride 1
|
|
*
|
|
* @param in Input matrix, size (1, w, h, c)
|
|
* @param filter 3x3 filter, size (1, 3, 3, c)
|
|
* @param bias Bias, size (1, 1, 1, n)
|
|
* @param padding Padding type, 0: valid, 1: same
|
|
* @param exponent Exponent for resulting matrix
|
|
* @param relu Whether to use relu activation
|
|
* @return Resulting quantized matrix
|
|
*/
|
|
dl_matrix3dq_t *dl_matrix3dqq_depthwise_conv_3x3s1_with_bias(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *f,
|
|
dl_matrix3dq_t *bias,
|
|
dl_padding_type padding,
|
|
int exponent,
|
|
int relu,
|
|
char *name);
|
|
|
|
dl_matrix3dq_t *dl_matrix3dqq_depthwise_conv_3x3_with_prelu(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *filter,
|
|
dl_matrix3dq_t *prelu,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
int exponent,
|
|
char *name);
|
|
|
|
dl_matrix3dq_t *dl_matrix3dqq_depthwise_conv_3x3_with_bias_prelu(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *f,
|
|
dl_matrix3dq_t *bias,
|
|
dl_matrix3dq_t *prelu,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
int exponent,
|
|
char *name);
|
|
|
|
dl_matrix3dq_t *dl_matrix3dqq_global_depthwise_conv_with_bias(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *filter,
|
|
dl_matrix3dq_t *bias,
|
|
int exponent,
|
|
char *name);
|
|
|
|
//
|
|
// Depthwise Common
|
|
//
|
|
#if CONFIG_DEVELOPING_CODE
|
|
dl_matrix3dq_t *dl_matrix3dqq_depthwise_conv_common(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *filter,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
int exponent,
|
|
dl_conv_mode mode);
|
|
|
|
dl_matrix3dq_t *dl_matrix3duq_depthwise_conv_common(dl_matrix3du_t *in,
|
|
dl_matrix3dq_t *filter,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
int exponent,
|
|
dl_conv_mode mode);
|
|
#endif
|
|
|
|
//
|
|
// Dot Product
|
|
//
|
|
|
|
void dl_matrix3dqq_dot_product(dl_matrix3dq_t *out,
|
|
dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *filter,
|
|
dl_conv_mode mode);
|
|
|
|
//
|
|
// FC
|
|
//
|
|
/**
|
|
* @brief Do fully connected layer forward.
|
|
*
|
|
* @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 mode Implementation mode
|
|
* @param name
|
|
*/
|
|
void dl_matrix3dqq_fc(dl_matrix3dq_t *out,
|
|
dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *filter,
|
|
dl_conv_mode mode,
|
|
char *name);
|
|
|
|
/**
|
|
* @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)
|
|
* @param mode Implementation mode
|
|
*/
|
|
void dl_matrix3dqq_fc_with_bias(dl_matrix3dq_t *out,
|
|
dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *filter,
|
|
dl_matrix3dq_t *bias,
|
|
dl_conv_mode mode,
|
|
char *name);
|
|
|
|
//
|
|
// Mobilefaceblock
|
|
//
|
|
/**
|
|
* @brief Do mobilefacenet process with splited pointwise 1x1 convolution, the process sequence is 1x1 pointwise->bn->relu->3x3 depthwise->bn->relu->1x1 pointwise->bn
|
|
*
|
|
* @param in Input matrix, size (1, w, h, c)
|
|
* @param pw_1 Pointwise 1x1 filter, size (n1/2, 1, 1, c)
|
|
* @param pw_2 Pointwise 1x1 filter, size (n1/2, 1, 1, c)
|
|
* @param pw_bias Pointwise bias, size (1, 1, 1, n1)
|
|
* @param dw Depthwise 3x3 filter, size (1, 3, 3, n1)
|
|
* @param dw_bias Depthwise bias, size (1, 1, 1, n1)
|
|
* @param pw_linear_1 Pointwise 1x1 filter, size (n2/2, 1, 1, n1)
|
|
* @param pw_linear_2 Pointwise 1x1 filter, size (n2/2, 1, 1, n1)
|
|
* @param pw_linear_bias Pointwise bias, size (1, 1, 1, n2)
|
|
* @param pw_exponent Exponent for pointwise resulting matrix
|
|
* @param dw_exponent Exponent for depthwise resulting matrix
|
|
* @param pw_linear_exponent Exponent for pointwise resulting matrix
|
|
* @param stride_x Stride of width
|
|
* @param stride_y Stride of height
|
|
* @param padding Padding type, 0: valid, 1: same
|
|
* @param mode Implementation mode
|
|
* @param shortcut Whether has a shortcut at pointwise linear
|
|
* @return Resulting quantized matrix
|
|
*/
|
|
dl_matrix3dq_t *dl_matrix3dqq_mobilefaceblock_split(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *pw_1,
|
|
dl_matrix3dq_t *pw_2,
|
|
dl_matrix3dq_t *pw_bias,
|
|
dl_matrix3dq_t *dw,
|
|
dl_matrix3dq_t *dw_bias,
|
|
dl_matrix3dq_t *pw_linear_1,
|
|
dl_matrix3dq_t *pw_linear_2,
|
|
dl_matrix3dq_t *pw_linear_bias,
|
|
int pw_exponent,
|
|
int dw_exponent,
|
|
int pw_linear_exponent,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
dl_conv_mode mode,
|
|
int shortcut);
|
|
|
|
/**
|
|
* @brief Do mobilefacenet process, the process sequence is 1x1 pointwise->bn->relu->3x3 depthwise->bn->relu->1x1 pointwise->bn
|
|
*
|
|
* @param in Input matrix, size (1, w, h, c)
|
|
* @param pw Pointwise 1x1 filter, size (n1, 1, 1, c)
|
|
* @param pw_bias Pointwise bias, size (1, 1, 1, n1)
|
|
* @param dw Depthwise 3x3 filter, size (1, 3, 3, n1)
|
|
* @param dw_bias Depthwise bias, size (1, 1, 1, n1)
|
|
* @param pw_linear Pointwise 1x1 filter, size (n2, 1, 1, n1)
|
|
* @param pw_linear_bias Pointwise bias, size (1, 1, 1, n2)
|
|
* @param pw_exponent Exponent for pointwise resulting matrix
|
|
* @param dw_exponent Exponent for depthwise resulting matrix
|
|
* @param pw_linear_exponent Exponent for pointwise resulting matrix
|
|
* @param stride_x Stride of width
|
|
* @param stride_y Stride of height
|
|
* @param padding Padding type, 0: valid, 1: same
|
|
* @param mode Implementation mode
|
|
* @param shortcut Whether has a shortcut at pointwise linear
|
|
* @return Resulting quantized matrix
|
|
*/
|
|
dl_matrix3dq_t *dl_matrix3dqq_mobilefaceblock(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *pw,
|
|
dl_matrix3dq_t *pw_bias,
|
|
dl_matrix3dq_t *dw,
|
|
dl_matrix3dq_t *dw_bias,
|
|
dl_matrix3dq_t *pw_linear,
|
|
dl_matrix3dq_t *pw_linear_bias,
|
|
int pw_exponent,
|
|
int dw_exponent,
|
|
int pw_linear_exponent,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
dl_conv_mode mode,
|
|
int shortcut);
|
|
|
|
dl_matrix3dq_t *dl_matrix3dqq_mobilefaceblock_prelu(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *pw,
|
|
dl_matrix3dq_t *pw_bias,
|
|
dl_matrix3dq_t *pw_prelu,
|
|
dl_matrix3dq_t *dw,
|
|
dl_matrix3dq_t *dw_bias,
|
|
dl_matrix3dq_t *dw_prelu,
|
|
dl_matrix3dq_t *pw_linear,
|
|
dl_matrix3dq_t *pw_linear_bias,
|
|
int pw_exponent,
|
|
int dw_exponent,
|
|
int pw_linear_exponent,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
dl_conv_mode mode,
|
|
int shortcut);
|
|
|
|
dl_matrix3dq_t *dl_matrix3dqq_mobilefaceblock_prelu_split_2_2(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *pw_1,
|
|
dl_matrix3dq_t *pw_2,
|
|
dl_matrix3dq_t *pw_bias,
|
|
dl_matrix3dq_t *pw_prelu,
|
|
dl_matrix3dq_t *dw,
|
|
dl_matrix3dq_t *dw_bias,
|
|
dl_matrix3dq_t *dw_prelu,
|
|
dl_matrix3dq_t *pw_linear_1,
|
|
dl_matrix3dq_t *pw_linear_2,
|
|
dl_matrix3dq_t *pw_linear_bias,
|
|
int pw_exponent,
|
|
int dw_exponent,
|
|
int pw_linear_exponent,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
dl_conv_mode mode,
|
|
int shortcut);
|
|
|
|
dl_matrix3dq_t *dl_matrix3dqq_mobilefaceblock_prelu_split_4_4(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *pw_1,
|
|
dl_matrix3dq_t *pw_2,
|
|
dl_matrix3dq_t *pw_3,
|
|
dl_matrix3dq_t *pw_4,
|
|
dl_matrix3dq_t *pw_bias,
|
|
dl_matrix3dq_t *pw_prelu,
|
|
dl_matrix3dq_t *dw,
|
|
dl_matrix3dq_t *dw_bias,
|
|
dl_matrix3dq_t *dw_prelu,
|
|
dl_matrix3dq_t *pw_linear_1,
|
|
dl_matrix3dq_t *pw_linear_2,
|
|
dl_matrix3dq_t *pw_linear_3,
|
|
dl_matrix3dq_t *pw_linear_4,
|
|
dl_matrix3dq_t *pw_linear_bias,
|
|
int pw_exponent,
|
|
int dw_exponent,
|
|
int pw_linear_exponent,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
dl_conv_mode mode,
|
|
int shortcut);
|
|
|
|
dl_matrix3dq_t *dl_matrix3dqq_mobilefaceblock_prelu_split_1_2(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *pw,
|
|
dl_matrix3dq_t *pw_bias,
|
|
dl_matrix3dq_t *pw_prelu,
|
|
dl_matrix3dq_t *dw,
|
|
dl_matrix3dq_t *dw_bias,
|
|
dl_matrix3dq_t *dw_prelu,
|
|
dl_matrix3dq_t *pw_linear_1,
|
|
dl_matrix3dq_t *pw_linear_2,
|
|
dl_matrix3dq_t *pw_linear_bias,
|
|
int pw_exponent,
|
|
int dw_exponent,
|
|
int pw_linear_exponent,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
dl_conv_mode mode,
|
|
int shortcut);
|
|
//
|
|
// Mobilenet
|
|
//
|
|
|
|
/**
|
|
* @brief Do mobilenet process, the process sequence is 1x1 dilated->prelu->3x3 depthwise->prelu->1x1 compress->bias
|
|
*
|
|
* @param in Input matrix, size (1, w, h, c)
|
|
* @param dilate Pointwise 1x1 filter, size (n1, 1, 1, c)
|
|
* @param dilate_prelu Pointwise prelu, size (1, 1, 1, n1)
|
|
* @param depthwise Depthwise 3x3 filter, size (1, 3, 3, n1)
|
|
* @param depthwise_prelu Depthwise prelu, size (1, 1, 1, n1)
|
|
* @param compress Pointwise 1x1 filter, size (n2, 1, 1, n1)
|
|
* @param bias Pointwise bias, size (1, 1, 1, n2)
|
|
* @param config Mobilenet configuration
|
|
* @return Resulting quantized matrix
|
|
*/
|
|
dl_matrix3dq_t *dl_matrix3dqq_mobilenet(dl_matrix3dq_t *in,
|
|
dl_matrix3dq_t *dilate,
|
|
dl_matrix3dq_t *dilate_prelu,
|
|
dl_matrix3dq_t *depthwise,
|
|
dl_matrix3dq_t *depth_prelu,
|
|
dl_matrix3dq_t *compress,
|
|
dl_matrix3dq_t *bias,
|
|
dl_matrix3dq_mobilenet_config_t config,
|
|
char *name);
|
|
|
|
/**
|
|
* @brief Do mobilenet process, the process sequence is 1x1 dilated->prelu->3x3 depthwise->prelu->1x1 compress->bias
|
|
*
|
|
* @param in Input matrix, 8-bit fixed point, size (1, w, h, c)
|
|
* @param dilate Pointwise 1x1 filter, size (n1, 1, 1, c)
|
|
* @param dilate_prelu Pointwise prelu, size (1, 1, 1, n1)
|
|
* @param depthwise Depthwise 3x3 filter, size (1, 3, 3, n1)
|
|
* @param depthwise_prelu Depthwise prelu, size (1, 1, 1, n1)
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* @param compress Pointwise 1x1 filter, size (n2, 1, 1, n1)
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* @param bias Pointwise bias, size (1, 1, 1, n2)
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* @param config Mobilenet configuration
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* @return Resulting quantized matrix
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|
*/
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dl_matrix3dq_t *dl_matrix3duq_mobilenet(dl_matrix3du_t *in,
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dl_matrix3dq_t *dilate,
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dl_matrix3dq_t *dilate_prelu,
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dl_matrix3dq_t *depthwise,
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dl_matrix3dq_t *depth_prelu,
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dl_matrix3dq_t *compress,
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dl_matrix3dq_t *bias,
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dl_matrix3dq_mobilenet_config_t config,
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char *name);
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|
|
|
//
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|
// Padding
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|
//
|
|
|
|
/**
|
|
* @brief
|
|
*
|
|
* @param padded_input
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* @param output_height
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* @param output_width
|
|
* @param input
|
|
* @param stride_x
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|
* @param stride_y
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|
* @param kernel_size
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|
* @param padding_type
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|
* @return dl_error_type
|
|
*/
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|
dl_error_type dl_matrix3dqq_padding(dl_matrix3dq_t **padded_input,
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|
int *output_height,
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|
int *output_width,
|
|
dl_matrix3dq_t *input,
|
|
int stride_x,
|
|
int stride_y,
|
|
int kernel_size,
|
|
dl_padding_type padding_type);
|
|
|
|
/**
|
|
* @brief
|
|
*
|
|
* @param padded_input
|
|
* @param output_height
|
|
* @param output_width
|
|
* @param input
|
|
* @param stride_x
|
|
* @param stride_y
|
|
* @param kernel_size
|
|
* @param padding_type
|
|
* @return dl_error_type
|
|
*/
|
|
dl_error_type dl_matrix3duq_padding(dl_matrix3du_t **padded_input,
|
|
int *output_height,
|
|
int *output_width,
|
|
dl_matrix3du_t *input,
|
|
int stride_x,
|
|
int stride_y,
|
|
int kernel_size,
|
|
dl_padding_type padding_type);
|
|
|
|
//
|
|
// Pooling
|
|
//
|
|
/**
|
|
* @brief Calculate average value of a feature map
|
|
*
|
|
* @param in Input matrix, size (1, w, h, c)
|
|
* @return Resulting matrix, size (1, 1, 1, c)
|
|
*/
|
|
dl_matrix3dq_t *dl_matrix3dq_global_pool(dl_matrix3dq_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 Resulting matrix, size (1, w', h', c)
|
|
*/
|
|
dl_matrix3dq_t *dl_matrix3dq_pooling(dl_matrix3dq_t *in,
|
|
int f_w,
|
|
int f_h,
|
|
int stride_x,
|
|
int stride_y,
|
|
dl_padding_type padding,
|
|
dl_pooling_type pooling_type);
|