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add onnx pack

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dkk0918
2021-09-06 21:50:44 +08:00
parent 427b13d14a
commit 6fb59b9313
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components/ai/onnx/operator_int/onnx.h Normal file
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#ifndef __ONNX_H__
#define __ONNX_H__
#include <stdio.h>
#include <stdint.h>
#include <string.h>
//#include <int.h>
#include <math.h>
#include <onnx-parser.h>
#define ONNX_USE_NWHC
#ifdef ONNX_USE_NWHC
// NWHC
#define W_INDEX 0
#define H_INDEX 1
#define C_INDEX 2
#else
// NCWH
#define C_INDEX 0
#define W_INDEX 1
#define H_INDEX 2
#endif
// Model
void onnx_tensor_info(const int* A, int64_t* shape, int64_t dim);
int* onnx_model_run(Onnx__ModelProto* model, int* input, int64_t* shapeInput);
// Layers
int* conv2D_layer(Onnx__GraphProto* graph, const int *input, int64_t* shapeInput, int64_t* shapeOutput, const char* layer_name);
int* relu_layer(Onnx__GraphProto* graph, const int *input, int64_t* shapeInput, int64_t* shapeOutput, const char* layer_name);
int* maxpool_layer(Onnx__GraphProto* graph, int* input, int64_t* shapeInput, int64_t* shapeOutput, const char* layer_name);
int* matmul_layer(Onnx__GraphProto* graph, const int *input, int64_t* shapeInput, int64_t* shapeOutput, const char* layer_name);
int* add_layer(Onnx__GraphProto* graph, const int *input, int64_t* shapeInput, int64_t* shapeOutput, const char* layer_name);
int* softmax_layer(Onnx__GraphProto* graph, const int *input, int64_t* shapeInput, int64_t* shapeOutput, const char* layer_name);
// Operators
int* transpose(const int* A, int64_t* shape, int64_t dim, int64_t* perm);
void conv2D(const int *input, // input image
const uint16_t dim_im_in_x, // input image dimention x
const uint16_t dim_im_in_y, // input image dimention y
const uint16_t ch_im_in, // number of input image channels
const int *weight, // kernel weights
const uint16_t ch_im_out, // number of filters, i.e., output image channels
const uint16_t dim_kernel_x, // filter kernel size x
const uint16_t dim_kernel_y, // filter kernel size y
const uint16_t padding_x, // padding sizes x
const uint16_t padding_y, // padding sizes y
const uint16_t stride_x, // stride x
const uint16_t stride_y, // stride y
const int *bias, // bias
int *output, // output image
const uint16_t dim_im_out_x, // output image dimension x
const uint16_t dim_im_out_y // output image dimension y
);
void relu(const int *input, uint32_t size, int* output);
void maxpool(const int *input,
const uint16_t dim_im_in_x, // input image dimension x or W
const uint16_t dim_im_in_y, // input image dimension y or H
const uint16_t ch_im_in, // number of input image channels
const uint16_t dim_kernel_x, // window kernel size
const uint16_t dim_kernel_y, // window kernel size
const uint16_t padding_x, // padding sizes
const uint16_t padding_y, // padding sizes
const uint16_t stride_x, // stride
const uint16_t stride_y, // stride
const uint16_t dim_im_out_x, // output image dimension x or W
const uint16_t dim_im_out_y, // output image dimension y or H
int *output);
void matmul(const int *input, // pointer to vector
const int *weight, // pointer to matrix
const uint16_t dim_vec, // length of the vector
const uint16_t num_of_rows, // numCol of A
int *output);
void add(const int *input, // pointer to vector
const int *bias, // pointer to matrix
const uint16_t dim_vec, // length of the vector
int *output);
void dense(const int *input, // pointer to vector
const int *weight, // pointer to matrix
const uint16_t dim_vec, // length of the vector
const uint16_t num_of_rows, // numCol of A
const int *bias,
int *output);
void softmax(const int *input, const uint32_t dim_vec, int *output);
#endif // __ONNX_H__