netrans/bin/vxcode/template/trial/vnn_pre_process.h.template

69 lines
1.4 KiB
Plaintext

/****************************************************************************
* Generated by NETRANS #NETRANS_VERSION#
* Match ovxlib #OVXLIB_VERSION#
*
* Neural Network appliction pre-process header file
****************************************************************************/
#ifndef _VNN_PRE_PROCESS_H_
#define _VNN_PRE_PROCESS_H_
typedef enum _vnn_file_type
{
NN_FILE_NONE,
NN_FILE_TENSOR,
NN_FILE_QTENSOR,
NN_FILE_JPG,
NN_FILE_BINARY
} vnn_file_type_e;
typedef enum _vnn_pre_order
{
VNN_PREPRO_NONE = -1,
VNN_PREPRO_REORDER,
VNN_PREPRO_MEAN,
VNN_PREPRO_SCALE,
VNN_PREPRO_NUM
} vnn_pre_order_e;
typedef struct _vnn_input_meta
{
union
{
struct
{
int32_t preprocess[VNN_PREPRO_NUM];
uint32_t reorder[#MAX_CHANNEL_COUNT#];
float mean[#MAX_CHANNEL_COUNT#];
float scale[#MAX_CHANNEL_COUNT#];
int32_t channel_count;
} image;
};
} vnn_input_meta_t;
vsi_status vnn_PreProcess#NETWORK_NAME#
(
vsi_nn_graph_t *graph,
const char **inputs,
uint32_t input_num
);
vsi_bool vnn_UseImagePreprocessNode();
void vnn_ReleaseBufferImage();
vsi_size_t vnn_LoadFP32DataFromTextFile
(
const char * fname,
uint8_t ** buffer_ptr,
vsi_size_t * buffer_sz
);
vsi_size_t vnn_LoadRawDataFromBinaryFile
(
const char * fname,
uint8_t ** buffer_ptr,
vsi_size_t * buffer_sz
);
#endif