forked from espressif/arduino-esp32
Update IDF to a8916daeb (#2992)
This commit is contained in:
Me No Dev
GitHub
@ -1,20 +1,19 @@
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#pragma once
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#include <stdint.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <assert.h>
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typedef float fptp_t;
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typedef uint8_t uc_t;
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typedef enum
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{
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DL_C_IMPL = 0,
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DL_XTENSA_IMPL = 1
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} dl_conv_mode;
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typedef enum
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{
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INPUT_UINT8 = 0,
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INPUT_FLOAT = 1,
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} dl_op_type;
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DL_SUCCESS = 0,
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DL_FAIL = 1,
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} dl_error_type;
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typedef enum
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{
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@ -53,9 +52,7 @@ typedef struct
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int stride_x;
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int stride_y;
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dl_padding_type padding;
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dl_conv_mode mode;
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dl_op_type type;
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} dl_matrix3d_conv_config_t;
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} dl_matrix3d_mobilenet_config_t;
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/*
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* @brief Allocate a 3D matrix with float items, the access sequence is NHWC
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@ -93,7 +90,6 @@ void dl_matrix3d_free(dl_matrix3d_t *m);
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*/
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void dl_matrix3du_free(dl_matrix3du_t *m);
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/*
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* @brief Dot product with a vector and matrix
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*
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@ -101,24 +97,7 @@ void dl_matrix3du_free(dl_matrix3du_t *m);
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* @param in input vector
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* @param f filter matrix
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*/
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void dl_matrix3d_dot_product(dl_matrix3d_t *out, dl_matrix3d_t *in, dl_matrix3d_t *f);
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/**
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* @brief Do a relu (Rectifier Linear Unit) operation, update the input matrix3d
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*
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* @param in Floating point input matrix3d
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* @param clip If value is higher than this, it will be clipped to this value
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*/
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void dl_matrix3d_relu(dl_matrix3d_t *m, fptp_t clip);
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/**
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* @brief Do a leaky relu (Rectifier Linear Unit) operation, update the input matrix3d
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*
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* @param in Floating point input matrix3d
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* @param clip If value is higher than this, it will be clipped to this value
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* @param alpha If value is less than zero, it will be updated by multiplying this factor
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*/
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void dl_matrix3d_leaky_relu(dl_matrix3d_t *m, fptp_t clip, fptp_t alpha);
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void dl_matrix3dff_dot_product(dl_matrix3d_t *out, dl_matrix3d_t *in, dl_matrix3d_t *f);
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/**
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* @brief Do a softmax operation on a matrix3d
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@ -127,18 +106,6 @@ void dl_matrix3d_leaky_relu(dl_matrix3d_t *m, fptp_t clip, fptp_t alpha);
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*/
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void dl_matrix3d_softmax(dl_matrix3d_t *m);
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/**
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* @brief Do a general fully connected layer pass, dimension is (number, width, height, channel)
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*
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* @param in Input matrix3d, size is (1, w, 1, 1)
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* @param filter Weights of the neurons, size is (1, w, h, 1)
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* @param bias Bias for the fc layer, size is (1, 1, 1, h)
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* @return The result of fc layer, size is (1, 1, 1, h)
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*/
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dl_matrix3d_t *dl_matrix3d_fc(dl_matrix3d_t *in,
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dl_matrix3d_t *filter,
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dl_matrix3d_t *bias);
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/**
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* @brief Copy a range of float items from an existing matrix to a preallocated matrix
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*
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@ -173,9 +140,6 @@ void dl_matrix3du_slice_copy(dl_matrix3du_t *dst,
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int w,
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int h);
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void dl_matrix3d_conv_1x1 (dl_matrix3d_t *out, dl_matrix3d_t *in, dl_matrix3d_t *f);
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/**
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* @brief Do a general CNN layer pass, dimension is (number, width, height, channel)
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*
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@ -197,11 +161,6 @@ dl_matrix3d_t *dl_matrix3d_conv(dl_matrix3d_t *in,
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int padding,
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int mode);
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void dl_matrix3d_conv_3x3_normal (dl_matrix3d_t *out,
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dl_matrix3d_t *in,
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dl_matrix3d_t *f,
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int step_x,
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int step_y);
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/**
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* @brief Do a general CNN layer pass, dimension is (number, width, height, channel)
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*
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@ -215,57 +174,6 @@ void dl_matrix3d_conv_3x3_normal (dl_matrix3d_t *out,
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* If ESP_PLATFORM is not defined, this value is not used. Default is 0
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* @return The result of CNN layer
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*/
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dl_matrix3d_t *dl_matrix3du_conv(dl_matrix3du_t *in,
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dl_matrix3d_t *filter,
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dl_matrix3d_t *bias,
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int stride_x,
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int stride_y,
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int padding,
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int mode);
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/**
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* @brief Do a depthwise CNN layer pass, dimension is (number, width, height, channel)
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*
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* @param in Input matrix3d
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* @param filter Weights of the neurons
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* @param stride_x The step length of the convolution window in x(width) direction
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* @param stride_y The step length of the convolution window in y(height) direction
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* @param padding One of VALID or SAME
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* @param mode Do convolution using C implement or xtensa implement, 0 or 1, with respect
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* If ESP_PLATFORM is not defined, this value is not used. Default is 0
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* @return The result of depthwise CNN layer
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*/
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dl_matrix3d_t *dl_matrix3d_depthwise_conv(dl_matrix3d_t *in,
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dl_matrix3d_t *filter,
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int stride_x,
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int stride_y,
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int padding,
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int mode);
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void dl_matrix3d_depthwise_conv_3x3_normal(dl_matrix3d_t *out,
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dl_matrix3d_t *in,
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dl_matrix3d_t *f,
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int step_x,
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int step_y);
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/**
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* @brief Do a mobilenet block forward, dimension is (number, width, height, channel)
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*
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* @param in Input matrix3d
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* @param filter Weights of the neurons
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* @param stride_x The step length of the convolution window in x(width) direction
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* @param stride_y The step length of the convolution window in y(height) direction
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* @param padding One of VALID or SAME
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* @param mode Do convolution using C implement or xtensa implement, 0 or 1, with respect
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* If ESP_PLATFORM is not defined, this value is not used. Default is 0
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* @return The result of depthwise CNN layer
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*/
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dl_matrix3d_t *dl_matrix3d_mobilenet(void *in,
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dl_matrix3d_t *dilate,
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dl_matrix3d_t *depthwise,
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dl_matrix3d_t *compress,
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dl_matrix3d_t *bias,
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dl_matrix3d_t *prelu,
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dl_matrix3d_conv_config_t *config);
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/**
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* @brief Do a global average pooling layer pass, dimension is (number, width, height, channel)
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@ -297,13 +205,6 @@ void dl_matrix3d_batch_normalize(dl_matrix3d_t *m,
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*/
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dl_matrix3d_t *dl_matrix3d_add(dl_matrix3d_t *in_1, dl_matrix3d_t *in_2);
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/**
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* @brief Do a standard relu operation, update the input matrix3d
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*
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* @param m Floating point input matrix3d
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*/
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void dl_matrix3d_relu_std(dl_matrix3d_t *m);
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/**
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* @brief Concatenate the channels of two matrix3ds into a new matrix3d
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*
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@ -372,7 +273,7 @@ dl_matrix3d_t *dl_matrix3d_concat_8(dl_matrix3d_t *in_1,
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* If ESP_PLATFORM is not defined, this value is not used. Default is 0
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* @return The result of a mobilefacenet block
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*/
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dl_matrix3d_t *dl_matrix3d_mobilefaceblock(void *in,
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dl_matrix3d_t *dl_matrix3d_mobilefaceblock(dl_matrix3d_t *in,
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dl_matrix3d_t *pw,
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dl_matrix3d_t *pw_bn_scale,
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dl_matrix3d_t *pw_bn_offset,
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@ -410,7 +311,7 @@ dl_matrix3d_t *dl_matrix3d_mobilefaceblock(void *in,
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* If ESP_PLATFORM is not defined, this value is not used. Default is 0
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* @return The result of a mobilefacenet block
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*/
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dl_matrix3d_t *dl_matrix3d_mobilefaceblock_split(void *in,
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dl_matrix3d_t *dl_matrix3d_mobilefaceblock_split(dl_matrix3d_t *in,
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dl_matrix3d_t *pw_1,
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dl_matrix3d_t *pw_2,
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dl_matrix3d_t *pw_bn_scale,
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@ -427,23 +328,200 @@ dl_matrix3d_t *dl_matrix3d_mobilefaceblock_split(void *in,
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int padding,
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int mode,
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int shortcut);
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/**
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* @brief Print the matrix3d items
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*
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* @param m dl_matrix3d_t to be printed
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* @param message name of matrix
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*/
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void dl_matrix3d_print(dl_matrix3d_t *m, char *message);
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/**
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* @brief Print the matrix3du items
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*
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* @param m dl_matrix3du_t to be printed
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* @param message name of matrix
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*/
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void dl_matrix3du_print(dl_matrix3du_t *m, char *message);
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void dl_matrix3d_init_bias (dl_matrix3d_t *out, dl_matrix3d_t *bias);
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void dl_matrix3d_init_bias(dl_matrix3d_t *out, dl_matrix3d_t *bias);
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void dl_matrix3d_multiply(dl_matrix3d_t *out, dl_matrix3d_t *in1, dl_matrix3d_t *in2);
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//
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// Activation
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//
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/**
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* @brief Do a standard relu operation, update the input matrix3d
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*
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* @param m Floating point input matrix3d
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*/
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void dl_matrix3d_relu(dl_matrix3d_t *m);
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/**
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* @brief Do a relu (Rectifier Linear Unit) operation, update the input matrix3d
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*
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* @param in Floating point input matrix3d
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* @param clip If value is higher than this, it will be clipped to this value
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*/
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void dl_matrix3d_relu_clip(dl_matrix3d_t *m, fptp_t clip);
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/**
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* @brief Do a Prelu (Rectifier Linear Unit) operation, update the input matrix3d
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*
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* @param in Floating point input matrix3d
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* @param alpha If value is less than zero, it will be updated by multiplying this factor
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*/
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void dl_matrix3d_p_relu(dl_matrix3d_t *in, dl_matrix3d_t *alpha);
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/**
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* @brief Do a leaky relu (Rectifier Linear Unit) operation, update the input matrix3d
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*
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* @param in Floating point input matrix3d
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* @param alpha If value is less than zero, it will be updated by multiplying this factor
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*/
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void dl_matrix3d_leaky_relu(dl_matrix3d_t *m, fptp_t alpha);
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//
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// Conv 1x1
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//
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void dl_matrix3dff_conv_1x1(dl_matrix3d_t *out,
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dl_matrix3d_t *in,
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dl_matrix3d_t *filter);
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void dl_matrix3dff_conv_1x1_with_bias(dl_matrix3d_t *out,
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dl_matrix3d_t *in,
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dl_matrix3d_t *filter,
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dl_matrix3d_t *bias);
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void dl_matrix3duf_conv_1x1(dl_matrix3d_t *out,
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dl_matrix3du_t *in,
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dl_matrix3d_t *filter);
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void dl_matrix3duf_conv_1x1_with_bias(dl_matrix3d_t *out,
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dl_matrix3du_t *in,
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dl_matrix3d_t *filter,
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dl_matrix3d_t *bias);
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//
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// Conv 3x3
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//
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void dl_matrix3dff_conv_3x3_op(dl_matrix3d_t *out,
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dl_matrix3d_t *in,
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dl_matrix3d_t *f,
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int step_x,
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int step_y);
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dl_matrix3d_t *dl_matrix3dff_conv_3x3(dl_matrix3d_t *in,
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dl_matrix3d_t *filter,
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dl_matrix3d_t *bias,
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int stride_x,
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int stride_y,
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dl_padding_type padding);
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//
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// Conv Common
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//
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dl_matrix3d_t *dl_matrix3duf_conv_common(dl_matrix3du_t *in,
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dl_matrix3d_t *filter,
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dl_matrix3d_t *bias,
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int stride_x,
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int stride_y,
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dl_padding_type padding);
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//
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// Depthwise 3x3
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//
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dl_matrix3d_t *dl_matrix3dff_depthwise_conv_3x3(dl_matrix3d_t *in,
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dl_matrix3d_t *filter,
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int stride_x,
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int stride_y,
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int padding);
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dl_matrix3d_t *dl_matrix3duf_depthwise_conv_3x3(dl_matrix3du_t *in,
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dl_matrix3d_t *filter,
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int stride_x,
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int stride_y,
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int padding);
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void dl_matrix3dff_depthwise_conv_3x3_op(dl_matrix3d_t *out,
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dl_matrix3d_t *in,
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dl_matrix3d_t *f,
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int step_x,
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int step_y);
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//
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// Depthwise Common
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//
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/**
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* @brief Do a depthwise CNN layer pass, dimension is (number, width, height, channel)
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*
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* @param in Input matrix3d
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* @param filter Weights of the neurons
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* @param stride_x The step length of the convolution window in x(width) direction
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* @param stride_y The step length of the convolution window in y(height) direction
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* @param padding One of VALID or SAME
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* @param mode Do convolution using C implement or xtensa implement, 0 or 1, with respect
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* If ESP_PLATFORM is not defined, this value is not used. Default is 0
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* @return The result of depthwise CNN layer
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*/
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dl_matrix3d_t *dl_matrix3dff_depthwise_conv_common(dl_matrix3d_t *in,
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dl_matrix3d_t *filter,
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int stride_x,
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int stride_y,
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dl_padding_type padding);
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//
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// FC
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//
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/**
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* @brief Do a general fully connected layer pass, dimension is (number, width, height, channel)
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*
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* @param in Input matrix3d, size is (1, w, 1, 1)
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* @param filter Weights of the neurons, size is (1, w, h, 1)
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* @param bias Bias for the fc layer, size is (1, 1, 1, h)
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* @return The result of fc layer, size is (1, 1, 1, h)
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*/
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void dl_matrix3dff_fc(dl_matrix3d_t *out,
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dl_matrix3d_t *in,
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dl_matrix3d_t *filter);
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void dl_matrix3dff_fc_with_bias(dl_matrix3d_t *out,
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dl_matrix3d_t *in,
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dl_matrix3d_t *filter,
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dl_matrix3d_t *bias);
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//
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// Mobilenet
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//
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/**
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* @brief Do a mobilenet block forward, dimension is (number, width, height, channel)
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*
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* @param in Input matrix3d
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* @param filter Weights of the neurons
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* @param stride_x The step length of the convolution window in x(width) direction
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* @param stride_y The step length of the convolution window in y(height) direction
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* @param padding One of VALID or SAME
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* @param mode Do convolution using C implement or xtensa implement, 0 or 1, with respect
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* If ESP_PLATFORM is not defined, this value is not used. Default is 0
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* @return The result of depthwise CNN layer
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*/
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dl_matrix3d_t *dl_matrix3dff_mobilenet(dl_matrix3d_t *in,
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dl_matrix3d_t *dilate_filter,
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dl_matrix3d_t *dilate_prelu,
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dl_matrix3d_t *depthwise_filter,
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dl_matrix3d_t *depthwise_prelu,
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dl_matrix3d_t *compress_filter,
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dl_matrix3d_t *bias,
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dl_matrix3d_mobilenet_config_t config);
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/**
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* @brief Do a mobilenet block forward, dimension is (number, width, height, channel)
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*
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* @param in Input matrix3du
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* @param filter Weights of the neurons
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* @param stride_x The step length of the convolution window in x(width) direction
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* @param stride_y The step length of the convolution window in y(height) direction
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* @param padding One of VALID or SAME
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* @param mode Do convolution using C implement or xtensa implement, 0 or 1, with respect
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* If ESP_PLATFORM is not defined, this value is not used. Default is 0
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* @return The result of depthwise CNN layer
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*/
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dl_matrix3d_t *dl_matrix3duf_mobilenet(dl_matrix3du_t *in,
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dl_matrix3d_t *dilate_filter,
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dl_matrix3d_t *dilate_prelu,
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dl_matrix3d_t *depthwise_filter,
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dl_matrix3d_t *depthwise_prelu,
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dl_matrix3d_t *compress_filter,
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dl_matrix3d_t *bias,
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dl_matrix3d_mobilenet_config_t config);
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