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rknn_usb_rtsp-master/ 这是RTSP推流的代码，用了rockcip硬件推流

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zhangpeng
2025-04-28 14:32:50 +08:00
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rknn_usb_rtsp-master/librknn_api/include/rknn_api.h Normal file
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/****************************************************************************
*
* Copyright (c) 2017 - 2022 by Rockchip Corp. All rights reserved.
*
* The material in this file is confidential and contains trade secrets
* of Rockchip Corporation. This is proprietary information owned by
* Rockchip Corporation. No part of this work may be disclosed,
* reproduced, copied, transmitted, or used in any way for any purpose,
* without the express written permission of Rockchip Corporation.
*
*****************************************************************************/
#ifndef _RKNN_API_H
#define _RKNN_API_H
#ifdef __cplusplus
extern "C" {
#endif
#include <stdint.h>
/*
Definition of extended flag for rknn_init.
*/
/* set high priority context. */
#define RKNN_FLAG_PRIOR_HIGH 0x00000000
/* set medium priority context */
#define RKNN_FLAG_PRIOR_MEDIUM 0x00000001
/* set low priority context. */
#define RKNN_FLAG_PRIOR_LOW 0x00000002
/* asynchronous mode.
when enable, rknn_outputs_get will not block for too long because it directly retrieves the result of
the previous frame which can increase the frame rate on single-threaded mode, but at the cost of
rknn_outputs_get not retrieves the result of the current frame.
in multi-threaded mode you do not need to turn this mode on. */
#define RKNN_FLAG_ASYNC_MASK 0x00000004
/* collect performance mode.
when enable, you can get detailed performance reports via rknn_query(ctx, RKNN_QUERY_PERF_DETAIL, ...),
but it will reduce the frame rate. */
#define RKNN_FLAG_COLLECT_PERF_MASK 0x00000008
/* allocate all memory in outside, includes weight/internal/inputs/outputs */
#define RKNN_FLAG_MEM_ALLOC_OUTSIDE 0x00000010
/* weight sharing with the same network structure */
#define RKNN_FLAG_SHARE_WEIGHT_MEM 0x00000020
/* send fence fd from outside */
#define RKNN_FLAG_FENCE_IN_OUTSIDE 0x00000040
/* get fence fd from inside */
#define RKNN_FLAG_FENCE_OUT_OUTSIDE 0x00000080
/* dummy init flag: could only get total_weight_size and total_internal_size by rknn_query*/
#define RKNN_FLAG_COLLECT_MODEL_INFO_ONLY 0x00000100
/* set GPU as the preferred execution backend When the operator is not supported by the NPU */
#define RKNN_FLAG_EXECUTE_FALLBACK_PRIOR_DEVICE_GPU 0x00000400
/* allocate internal memory in outside */
#define RKNN_FLAG_INTERNAL_ALLOC_OUTSIDE 0x00000200
/*
Error code returned by the RKNN API.
*/
#define RKNN_SUCC 0 /* execute succeed. */
#define RKNN_ERR_FAIL -1 /* execute failed. */
#define RKNN_ERR_TIMEOUT -2 /* execute timeout. */
#define RKNN_ERR_DEVICE_UNAVAILABLE -3 /* device is unavailable. */
#define RKNN_ERR_MALLOC_FAIL -4 /* memory malloc fail. */
#define RKNN_ERR_PARAM_INVALID -5 /* parameter is invalid. */
#define RKNN_ERR_MODEL_INVALID -6 /* model is invalid. */
#define RKNN_ERR_CTX_INVALID -7 /* context is invalid. */
#define RKNN_ERR_INPUT_INVALID -8 /* input is invalid. */
#define RKNN_ERR_OUTPUT_INVALID -9 /* output is invalid. */
#define RKNN_ERR_DEVICE_UNMATCH -10 /* the device is unmatch, please update rknn sdk
and npu driver/firmware. */
#define RKNN_ERR_INCOMPATILE_PRE_COMPILE_MODEL -11 /* This RKNN model use pre_compile mode, but not compatible with current driver. */
#define RKNN_ERR_INCOMPATILE_OPTIMIZATION_LEVEL_VERSION -12 /* This RKNN model set optimization level, but not compatible with current driver. */
#define RKNN_ERR_TARGET_PLATFORM_UNMATCH -13 /* This RKNN model set target platform, but not compatible with current platform. */
/*
Definition for tensor
*/
#define RKNN_MAX_DIMS 16 /* maximum dimension of tensor. */
#define RKNN_MAX_NUM_CHANNEL 15 /* maximum channel number of input tensor. */
#define RKNN_MAX_NAME_LEN 256 /* maximum name lenth of tensor. */
#define RKNN_MAX_DYNAMIC_SHAPE_NUM 512 /* maximum number of dynamic shape for each input. */
#ifdef __arm__
typedef uint32_t rknn_context;
#else
typedef uint64_t rknn_context;
#endif
/*
The query command for rknn_query
*/
typedef enum _rknn_query_cmd {
RKNN_QUERY_IN_OUT_NUM = 0, /* query the number of input & output tensor. */
RKNN_QUERY_INPUT_ATTR = 1, /* query the attribute of input tensor. */
RKNN_QUERY_OUTPUT_ATTR = 2, /* query the attribute of output tensor. */
RKNN_QUERY_PERF_DETAIL = 3, /* query the detail performance, need set
RKNN_FLAG_COLLECT_PERF_MASK when call rknn_init,
this query needs to be valid after rknn_outputs_get. */
RKNN_QUERY_PERF_RUN = 4, /* query the time of run,
this query needs to be valid after rknn_outputs_get. */
RKNN_QUERY_SDK_VERSION = 5, /* query the sdk & driver version */
RKNN_QUERY_MEM_SIZE = 6, /* query the weight & internal memory size */
RKNN_QUERY_CUSTOM_STRING = 7, /* query the custom string */
RKNN_QUERY_NATIVE_INPUT_ATTR = 8, /* query the attribute of native input tensor. */
RKNN_QUERY_NATIVE_OUTPUT_ATTR = 9, /* query the attribute of native output tensor. */
RKNN_QUERY_NATIVE_NC1HWC2_INPUT_ATTR = 8, /* query the attribute of native input tensor. */
RKNN_QUERY_NATIVE_NC1HWC2_OUTPUT_ATTR = 9, /* query the attribute of native output tensor. */
RKNN_QUERY_NATIVE_NHWC_INPUT_ATTR = 10, /* query the attribute of native input tensor. */
RKNN_QUERY_NATIVE_NHWC_OUTPUT_ATTR = 11, /* query the attribute of native output tensor. */
RKNN_QUERY_DEVICE_MEM_INFO = 12, /* query the attribute of rknn memory information. */
RKNN_QUERY_INPUT_DYNAMIC_RANGE = 13, /* query the dynamic shape range of rknn input tensor. */
RKNN_QUERY_CURRENT_INPUT_ATTR = 14, /* query the current shape of rknn input tensor, only valid for dynamic rknn model*/
RKNN_QUERY_CURRENT_OUTPUT_ATTR = 15, /* query the current shape of rknn output tensor, only valid for dynamic rknn model*/
RKNN_QUERY_CURRENT_NATIVE_INPUT_ATTR = 16, /* query the current native shape of rknn input tensor, only valid for dynamic rknn model*/
RKNN_QUERY_CURRENT_NATIVE_OUTPUT_ATTR = 17, /* query the current native shape of rknn output tensor, only valid for dynamic rknn model*/
RKNN_QUERY_CMD_MAX
} rknn_query_cmd;
/*
the tensor data type.
*/
typedef enum _rknn_tensor_type {
RKNN_TENSOR_FLOAT32 = 0, /* data type is float32. */
RKNN_TENSOR_FLOAT16, /* data type is float16. */
RKNN_TENSOR_INT8, /* data type is int8. */
RKNN_TENSOR_UINT8, /* data type is uint8. */
RKNN_TENSOR_INT16, /* data type is int16. */
RKNN_TENSOR_UINT16, /* data type is uint16. */
RKNN_TENSOR_INT32, /* data type is int32. */
RKNN_TENSOR_UINT32, /* data type is uint32. */
RKNN_TENSOR_INT64, /* data type is int64. */
RKNN_TENSOR_BOOL,
RKNN_TENSOR_TYPE_MAX
} rknn_tensor_type;
inline static const char* get_type_string(rknn_tensor_type type)
{
switch(type) {
case RKNN_TENSOR_FLOAT32: return "FP32";
case RKNN_TENSOR_FLOAT16: return "FP16";
case RKNN_TENSOR_INT8: return "INT8";
case RKNN_TENSOR_UINT8: return "UINT8";
case RKNN_TENSOR_INT16: return "INT16";
case RKNN_TENSOR_UINT16: return "UINT16";
case RKNN_TENSOR_INT32: return "INT32";
case RKNN_TENSOR_UINT32: return "UINT32";
case RKNN_TENSOR_INT64: return "INT64";
case RKNN_TENSOR_BOOL: return "BOOL";
default: return "UNKNOW";
}
}
/*
the quantitative type.
*/
typedef enum _rknn_tensor_qnt_type {
RKNN_TENSOR_QNT_NONE = 0, /* none. */
RKNN_TENSOR_QNT_DFP, /* dynamic fixed point. */
RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC, /* asymmetric affine. */
RKNN_TENSOR_QNT_MAX
} rknn_tensor_qnt_type;
inline static const char* get_qnt_type_string(rknn_tensor_qnt_type type)
{
switch(type) {
case RKNN_TENSOR_QNT_NONE: return "NONE";
case RKNN_TENSOR_QNT_DFP: return "DFP";
case RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC: return "AFFINE";
default: return "UNKNOW";
}
}
/*
the tensor data format.
*/
typedef enum _rknn_tensor_format {
RKNN_TENSOR_NCHW = 0, /* data format is NCHW. */
RKNN_TENSOR_NHWC, /* data format is NHWC. */
RKNN_TENSOR_NC1HWC2, /* data format is NC1HWC2. */
RKNN_TENSOR_UNDEFINED,
RKNN_TENSOR_FORMAT_MAX
} rknn_tensor_format;
/*
the mode of running on target NPU core.
*/
typedef enum _rknn_core_mask {
RKNN_NPU_CORE_AUTO = 0, /* default, run on NPU core randomly. */
RKNN_NPU_CORE_0 = 1, /* run on NPU core 0. */
RKNN_NPU_CORE_1 = 2, /* run on NPU core 1. */
RKNN_NPU_CORE_2 = 4, /* run on NPU core 2. */
RKNN_NPU_CORE_0_1 = RKNN_NPU_CORE_0 | RKNN_NPU_CORE_1, /* run on NPU core 0 and core 1. */
RKNN_NPU_CORE_0_1_2 = RKNN_NPU_CORE_0_1 | RKNN_NPU_CORE_2, /* run on NPU core 0 and core 1 and core 2. */
RKNN_NPU_CORE_UNDEFINED,
} rknn_core_mask;
inline static const char* get_format_string(rknn_tensor_format fmt)
{
switch(fmt) {
case RKNN_TENSOR_NCHW: return "NCHW";
case RKNN_TENSOR_NHWC: return "NHWC";
case RKNN_TENSOR_NC1HWC2: return "NC1HWC2";
case RKNN_TENSOR_UNDEFINED: return "UNDEFINED";
default: return "UNKNOW";
}
}
/*
the information for RKNN_QUERY_IN_OUT_NUM.
*/
typedef struct _rknn_input_output_num {
uint32_t n_input; /* the number of input. */
uint32_t n_output; /* the number of output. */
} rknn_input_output_num;
/*
the information for RKNN_QUERY_INPUT_ATTR / RKNN_QUERY_OUTPUT_ATTR.
*/
typedef struct _rknn_tensor_attr {
uint32_t index; /* input parameter, the index of input/output tensor,
need set before call rknn_query. */
uint32_t n_dims; /* the number of dimensions. */
uint32_t dims[RKNN_MAX_DIMS]; /* the dimensions array. */
char name[RKNN_MAX_NAME_LEN]; /* the name of tensor. */
uint32_t n_elems; /* the number of elements. */
uint32_t size; /* the bytes size of tensor. */
rknn_tensor_format fmt; /* the data format of tensor. */
rknn_tensor_type type; /* the data type of tensor. */
rknn_tensor_qnt_type qnt_type; /* the quantitative type of tensor. */
int8_t fl; /* fractional length for RKNN_TENSOR_QNT_DFP. */
int32_t zp; /* zero point for RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC. */
float scale; /* scale for RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC. */
uint32_t w_stride; /* the stride of tensor along the width dimention of input,
Note: it is read-only, 0 means equal to width. */
uint32_t size_with_stride; /* the bytes size of tensor with stride. */
uint8_t pass_through; /* pass through mode, for rknn_set_io_mem interface.
if TRUE, the buf data is passed directly to the input node of the rknn model
without any conversion. the following variables do not need to be set.
if FALSE, the buf data is converted into an input consistent with the model
according to the following type and fmt. so the following variables
need to be set.*/
uint32_t h_stride; /* the stride along the height dimention of input,
Note: it is write-only, if it was set to 0, h_stride = height. */
} rknn_tensor_attr;
typedef struct _rknn_input_range {
uint32_t index; /* input parameter, the index of input/output tensor,
need set before call rknn_query. */
uint32_t shape_number; /* the number of shape. */
rknn_tensor_format fmt; /* the data format of tensor. */
char name[RKNN_MAX_NAME_LEN]; /* the name of tensor. */
uint32_t dyn_range[RKNN_MAX_DYNAMIC_SHAPE_NUM][RKNN_MAX_DIMS]; /* the dynamic input dimensions range. */
uint32_t n_dims; /* the number of dimensions. */
} rknn_input_range;
/*
the information for RKNN_QUERY_PERF_DETAIL.
*/
typedef struct _rknn_perf_detail {
char* perf_data; /* the string pointer of perf detail. don't need free it by user. */
uint64_t data_len; /* the string length. */
} rknn_perf_detail;
/*
the information for RKNN_QUERY_PERF_RUN.
*/
typedef struct _rknn_perf_run {
int64_t run_duration; /* real inference time (us) */
} rknn_perf_run;
/*
the information for RKNN_QUERY_SDK_VERSION.
*/
typedef struct _rknn_sdk_version {
char api_version[256]; /* the version of rknn api. */
char drv_version[256]; /* the version of rknn driver. */
} rknn_sdk_version;
/*
the information for RKNN_QUERY_MEM_SIZE.
*/
typedef struct _rknn_mem_size {
uint32_t total_weight_size; /* the weight memory size */
uint32_t total_internal_size; /* the internal memory size, exclude inputs/outputs */
uint64_t total_dma_allocated_size; /* total dma memory allocated size */
uint32_t total_sram_size; /* total system sram size reserved for rknn */
uint32_t free_sram_size; /* free system sram size reserved for rknn */
uint32_t reserved[10]; /* reserved */
} rknn_mem_size;
/*
the information for RKNN_QUERY_CUSTOM_STRING.
*/
typedef struct _rknn_custom_string {
char string[1024]; /* the string of custom, lengths max to 1024 bytes */
} rknn_custom_string;
/*
The flags of rknn_tensor_mem.
*/
typedef enum _rknn_tensor_mem_flags {
RKNN_TENSOR_MEMORY_FLAGS_ALLOC_INSIDE = 1, /*Used to mark in rknn_destroy_mem() whether it is necessary to release the "mem" pointer itself.
If the flag RKNN_TENSOR_MEMORY_FLAGS_ALLOC_INSIDE is set, rknn_destroy_mem() will call free(mem).*/
RKNN_TENSOR_MEMORY_FLAGS_FROM_FD = 2, /*Used to mark in rknn_create_mem_from_fd() whether it is necessary to release the "mem" pointer itself.
If the flag RKNN_TENSOR_MEMORY_FLAGS_FROM_FD is set, rknn_destroy_mem() will call free(mem).*/
RKNN_TENSOR_MEMORY_FLAGS_FROM_PHYS = 3, /*Used to mark in rknn_create_mem_from_phys() whether it is necessary to release the "mem" pointer itself.
If the flag RKNN_TENSOR_MEMORY_FLAGS_FROM_PHYS is set, rknn_destroy_mem() will call free(mem).*/
RKNN_TENSOR_MEMORY_FLAGS_UNKNOWN
} rknn_tensor_mem_flags;
/*
the memory information of tensor.
*/
typedef struct _rknn_tensor_memory {
void* virt_addr; /* the virtual address of tensor buffer. */
uint64_t phys_addr; /* the physical address of tensor buffer. */
int32_t fd; /* the fd of tensor buffer. */
int32_t offset; /* indicates the offset of the memory. */
uint32_t size; /* the size of tensor buffer. */
uint32_t flags; /* the flags of tensor buffer, reserved */
void * priv_data; /* the private data of tensor buffer. */
} rknn_tensor_mem;
/*
the input information for rknn_input_set.
*/
typedef struct _rknn_input {
uint32_t index; /* the input index. */
void* buf; /* the input buf for index. */
uint32_t size; /* the size of input buf. */
uint8_t pass_through; /* pass through mode.
if TRUE, the buf data is passed directly to the input node of the rknn model
without any conversion. the following variables do not need to be set.
if FALSE, the buf data is converted into an input consistent with the model
according to the following type and fmt. so the following variables
need to be set.*/
rknn_tensor_type type; /* the data type of input buf. */
rknn_tensor_format fmt; /* the data format of input buf.
currently the internal input format of NPU is NCHW by default.
so entering NCHW data can avoid the format conversion in the driver. */
} rknn_input;
/*
the output information for rknn_outputs_get.
*/
typedef struct _rknn_output {
uint8_t want_float; /* want transfer output data to float */
uint8_t is_prealloc; /* whether buf is pre-allocated.
if TRUE, the following variables need to be set.
if FALSE, the following variables do not need to be set. */
uint32_t index; /* the output index. */
void* buf; /* the output buf for index.
when is_prealloc = FALSE and rknn_outputs_release called,
this buf pointer will be free and don't use it anymore. */
uint32_t size; /* the size of output buf. */
} rknn_output;
/*
the extend information for rknn_init.
*/
typedef struct _rknn_init_extend {
rknn_context ctx; /* rknn context */
int32_t real_model_offset; /* real rknn model file offset, only valid when init context with rknn file path */
uint32_t real_model_size; /* real rknn model file size, only valid when init context with rknn file path */
uint8_t reserved[120]; /* reserved */
} rknn_init_extend;
/*
the extend information for rknn_run.
*/
typedef struct _rknn_run_extend {
uint64_t frame_id; /* output parameter, indicate current frame id of run. */
int32_t non_block; /* block flag of run, 0 is block else 1 is non block */
int32_t timeout_ms; /* timeout for block mode, in milliseconds */
int32_t fence_fd; /* fence fd from other unit */
} rknn_run_extend;
/*
the extend information for rknn_outputs_get.
*/
typedef struct _rknn_output_extend {
uint64_t frame_id; /* output parameter, indicate the frame id of outputs, corresponds to
struct rknn_run_extend.frame_id.*/
} rknn_output_extend;
/* rknn_init
initial the context and load the rknn model.
input:
rknn_context* context the pointer of context handle.
void* model if size > 0, pointer to the rknn model, if size = 0, filepath to the rknn model.
uint32_t size the size of rknn model.
uint32_t flag extend flag, see the define of RKNN_FLAG_XXX_XXX.
rknn_init_extend* extend the extend information of init.
return:
int error code.
*/
int rknn_init(rknn_context* context, void* model, uint32_t size, uint32_t flag, rknn_init_extend* extend);
/* rknn_dup_context
initial the context and load the rknn model.
input:
rknn_context* context_in the pointer of context in handle.
rknn_context* context_out the pointer of context out handle.
return:
int error code.
*/
int rknn_dup_context(rknn_context* context_in, rknn_context* context_out);
/* rknn_destroy
unload the rknn model and destroy the context.
input:
rknn_context context the handle of context.
return:
int error code.
*/
int rknn_destroy(rknn_context context);
/* rknn_query
query the information about model or others. see rknn_query_cmd.
input:
rknn_context context the handle of context.
rknn_query_cmd cmd the command of query.
void* info the buffer point of information.
uint32_t size the size of information.
return:
int error code.
*/
int rknn_query(rknn_context context, rknn_query_cmd cmd, void* info, uint32_t size);
/* rknn_inputs_set
set inputs information by input index of rknn model.
inputs information see rknn_input.
input:
rknn_context context the handle of context.
uint32_t n_inputs the number of inputs.
rknn_input inputs[] the arrays of inputs information, see rknn_input.
return:
int error code
*/
int rknn_inputs_set(rknn_context context, uint32_t n_inputs, rknn_input inputs[]);
/*
rknn_set_batch_core_num
set rknn batch core_num.
input:
rknn_context context the handle of context.
int core_num the core number.
return:
int error code.
*/
int rknn_set_batch_core_num(rknn_context context, int core_num);
/* rknn_set_core_mask
set rknn core mask.(only supported on RK3588 now)
RKNN_NPU_CORE_AUTO: auto mode, default value
RKNN_NPU_CORE_0: core 0 mode
RKNN_NPU_CORE_1: core 1 mode
RKNN_NPU_CORE_2: core 2 mode
RKNN_NPU_CORE_0_1: combine core 0/1 mode
RKNN_NPU_CORE_0_1_2: combine core 0/1/2 mode
input:
rknn_context context the handle of context.
rknn_core_mask core_mask the core mask.
return:
int error code.
*/
int rknn_set_core_mask(rknn_context context, rknn_core_mask core_mask);
/* rknn_run
run the model to execute inference.
input:
rknn_context context the handle of context.
rknn_run_extend* extend the extend information of run.
return:
int error code.
*/
int rknn_run(rknn_context context, rknn_run_extend* extend);
/* rknn_wait
wait the model after execute inference.
input:
rknn_context context the handle of context.
rknn_run_extend* extend the extend information of run.
return:
int error code.
*/
int rknn_wait(rknn_context context, rknn_run_extend* extend);
/* rknn_outputs_get
wait the inference to finish and get the outputs.
this function will block until inference finish.
the results will set to outputs[].
input:
rknn_context context the handle of context.
uint32_t n_outputs the number of outputs.
rknn_output outputs[] the arrays of output, see rknn_output.
rknn_output_extend* the extend information of output.
return:
int error code.
*/
int rknn_outputs_get(rknn_context context, uint32_t n_outputs, rknn_output outputs[], rknn_output_extend* extend);
/* rknn_outputs_release
release the outputs that get by rknn_outputs_get.
after called, the rknn_output[x].buf get from rknn_outputs_get will
also be free when rknn_output[x].is_prealloc = FALSE.
input:
rknn_context context the handle of context.
uint32_t n_ouputs the number of outputs.
rknn_output outputs[] the arrays of output.
return:
int error code
*/
int rknn_outputs_release(rknn_context context, uint32_t n_ouputs, rknn_output outputs[]);
/* new api for zero copy */
/* rknn_create_mem_from_phys (memory allocated outside)
initialize tensor memory from physical address.
input:
rknn_context ctx the handle of context.
uint64_t phys_addr physical address.
void *virt_addr virtual address.
uint32_t size the size of tensor buffer.
return:
rknn_tensor_mem the pointer of tensor memory information.
*/
rknn_tensor_mem* rknn_create_mem_from_phys(rknn_context ctx, uint64_t phys_addr, void *virt_addr, uint32_t size);
/* rknn_create_mem_from_fd (memory allocated outside)
initialize tensor memory from file description.
input:
rknn_context ctx the handle of context.
int32_t fd file description.
void *virt_addr virtual address.
uint32_t size the size of tensor buffer.
int32_t offset indicates the offset of the memory (virt_addr without offset).
return:
rknn_tensor_mem the pointer of tensor memory information.
*/
rknn_tensor_mem* rknn_create_mem_from_fd(rknn_context ctx, int32_t fd, void *virt_addr, uint32_t size, int32_t offset);
/* rknn_create_mem_from_mb_blk (memory allocated outside)
create tensor memory from mb_blk.
input:
rknn_context ctx the handle of context.
void *mb_blk mb_blk allocate from system api.
int32_t offset indicates the offset of the memory.
return:
rknn_tensor_mem the pointer of tensor memory information.
*/
rknn_tensor_mem* rknn_create_mem_from_mb_blk(rknn_context ctx, void *mb_blk, int32_t offset);
/* rknn_create_mem (memory allocated inside)
create tensor memory.
input:
rknn_context ctx the handle of context.
uint32_t size the size of tensor buffer.
return:
rknn_tensor_mem the pointer of tensor memory information.
*/
rknn_tensor_mem* rknn_create_mem(rknn_context ctx, uint32_t size);
/* rknn_destroy_mem (support allocate inside and outside)
destroy tensor memory.
input:
rknn_context ctx the handle of context.
rknn_tensor_mem *mem the pointer of tensor memory information.
return:
int error code
*/
int rknn_destroy_mem(rknn_context ctx, rknn_tensor_mem *mem);
/* rknn_set_weight_mem
set the weight memory.
input:
rknn_context ctx the handle of context.
rknn_tensor_mem *mem the array of tensor memory information
return:
int error code.
*/
int rknn_set_weight_mem(rknn_context ctx, rknn_tensor_mem *mem);
/* rknn_set_internal_mem
set the internal memory.
input:
rknn_context ctx the handle of context.
rknn_tensor_mem *mem the array of tensor memory information
return:
int error code.
*/
int rknn_set_internal_mem(rknn_context ctx, rknn_tensor_mem *mem);
/* rknn_set_io_mem
set the input and output tensors buffer.
input:
rknn_context ctx the handle of context.
rknn_tensor_mem *mem the array of tensor memory information.
rknn_tensor_attr *attr the attribute of input or output tensor buffer.
return:
int error code.
*/
int rknn_set_io_mem(rknn_context ctx, rknn_tensor_mem *mem, rknn_tensor_attr *attr);
/* rknn_set_input_shape(deprecated)
set the input tensor shape (only valid for dynamic shape rknn model).
input:
rknn_context ctx the handle of context.
rknn_tensor_attr *attr the attribute of input or output tensor buffer.
return:
int error code.
*/
int rknn_set_input_shape(rknn_context ctx, rknn_tensor_attr* attr);
/* rknn_set_input_shapes
set all the input tensor shapes. graph will run under current set of input shapes after rknn_set_input_shapes.(only valid for dynamic shape rknn model).
input:
rknn_context ctx the handle of context.
uint32_t n_inputs the number of inputs.
rknn_tensor_attr attr[] the attribute array of all input tensors.
return:
int error code.
*/
int rknn_set_input_shapes(rknn_context ctx, uint32_t n_inputs, rknn_tensor_attr attr[]);
#ifdef __cplusplus
} //extern "C"
#endif
#endif //_RKNN_API_H

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/****************************************************************************
*
* Copyright (c) 2017 - 2018 by Rockchip Corp. All rights reserved.
*
* The material in this file is confidential and contains trade secrets
* of Rockchip Corporation. This is proprietary information owned by
* Rockchip Corporation. No part of this work may be disclosed,
* reproduced, copied, transmitted, or used in any way for any purpose,
* without the express written permission of Rockchip Corporation.
*
*****************************************************************************/
#ifndef _RKNN_MATMUL_API_H
#define _RKNN_MATMUL_API_H
#ifdef __cplusplus
extern "C" {
#endif
#include "rknn_api.h"
typedef rknn_context rknn_matmul_ctx;
typedef struct _rknn_matmul_tensor_attr
{
char name[RKNN_MAX_NAME_LEN];
// indicate A(M, K) or B(K, N) or C(M, N)
uint32_t n_dims;
uint32_t dims[RKNN_MAX_DIMS];
// matmul tensor size
uint32_t size;
// matmul tensor data type
// int8 : A, B
// int32: C
rknn_tensor_type type;
} rknn_matmul_tensor_attr;
typedef struct _rknn_matmul_io_attr
{
// indicate A(M, K) or B(K, N) or C(M, N)
rknn_matmul_tensor_attr A;
rknn_matmul_tensor_attr B;
rknn_matmul_tensor_attr C;
} rknn_matmul_io_attr;
/*
matmul information struct
*/
typedef struct rknn_matmul_info_t
{
int32_t M;
int32_t K; // limit: rk356x: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte;
// rk3588: int8 type must be aligned with 32byte, float16 type must be aligned with 32byte;
int32_t N; // limit: rk356x: int8 type must be aligned with 16byte, float16 type must be aligned with 8byte;
// rk3588: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte;
// matmul data type
// int8: int8(A) x int8(B) -> int32(C)
// float16: float16(A) x float16(B) -> float32(C)
rknn_tensor_type type;
// matmul native layout for B
// 0: normal layout
// 1: native layout
int32_t native_layout;
// matmul perf layout for A and C
// 0: normal layout
// 1: perf layout
int32_t perf_layout;
} rknn_matmul_info;
/* rknn_matmul_create
params:
rknn_matmul_ctx *ctx the handle of context.
rknn_matmul_info *info the matmal information.
rknn_matmul_io_attr *io_attr inputs/output attribute
return:
int error code
*/
int rknn_matmul_create(rknn_matmul_ctx* ctx, rknn_matmul_info* info, rknn_matmul_io_attr* io_attr);
/* rknn_matmul_set_io_mem
params:
rknn_matmul_ctx ctx the handle of context.
rknn_tensor_mem *mem the pointer of tensor memory information.
rknn_matmul_tensor_attr *attr the attribute of input or output tensor buffer.
return:
int error code.
formula:
C = A * B,
limit:
K <= 4096
K limit: rk356x: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte;
rk3588: int8 type must be aligned with 32byte, float16 type must be aligned with 32byte;
N limit: rk356x: int8 type must be aligned with 16byte, float16 type must be aligned with 8byte;
rk3588: int8 type must be aligned with 32byte, float16 type must be aligned with 16byte;
A shape: M x K
normal layout: (M, K)
[M1K1, M1K2, ..., M1Kk,
M2K1, M2K2, ..., M2Kk,
...
MmK1, MmK2, ..., MmKk]
for rk356x：
int8:
perf layout: (K / 8, M, 8)
[K1M1, K2M1, ..., K8M1,
K9M2, K10M2, ..., K16M2,
...
K(k-7)Mm, K(k-6)Mm, ..., KkMm]
float16:
perf layout: (K / 4, M, 4)
[K1M1, K2M1, ..., K4M1,
K9M2, K10M2, ..., K8M2,
...
K(k-3)Mm, K(k-2)Mm, ..., KkMm]
for rk3588：
int8:
perf layout: (K / 16, M, 16)
[K1M1, K2M1, ..., K16M1,
K9M2, K10M2, ..., K32M2,
...
K(k-15)Mm, K(k-14)Mm, ..., KkMm]
float16:
perf layout: (K / 8, M, 8)
[K1M1, K2M1, ..., K8M1,
K9M2, K10M2, ..., K16M2,
...
K(k-7)Mm, K(k-6)Mm, ..., KkMm]
B shape: K x N
normal layout: (K, N)
[K1N1, K1N2, ..., K1Nn,
K2N1, K2N2, ..., K2Nn,
...
KkN1, KkN2, ..., KkNn]
for rk356x：
int8:
native layout: (N / 16, K / 32, 16, 32)
[K1N1, K2N1, ..., K32N1,
K1N2, K2N2, ..., K32N2,
...
K1N16, K2N16, ..., K32N16,
K33N1, K34N1, ..., K64N1,
K33N2, K34N2, ..., K64N2,
...
K(k-31)N16, K(k-30)N16, ..., KkN16,
K1N17, K2N17, ..., K32N17,
K1N18, K2N18, ..., K32N18,
...
K(k-31)Nn, K(k-30)Nn, ..., KkNn]
float16:
native layout: (N / 8, K / 16, 8, 16)
[K1N1, K2N1, ..., K16N1,
K1N2, K2N2, ..., K16N2,
...
K1N8, K2N8, ..., K16N8,
K17N1, K18N1, ..., K32N1,
K17N2, K18N2, ..., K32N2,
...
K(k-15)N8, K(k-30)N8, ..., KkN8,
K1N9, K2N9, ..., K16N9,
K1N10, K2N10, ..., K16N10,
...
K(k-15)Nn, K(k-14)Nn, ..., KkNn]
for rk3588：
int8:
native layout: (N / 32, K / 32, 32, 32)
[K1N1, K2N1, ..., K32N1,
K1N2, K2N2, ..., K32N2,
...
K1N32, K2N32, ..., K32N32,
K33N1, K34N1, ..., K64N1,
K33N2, K34N2, ..., K64N2,
...
K(k-31)N32, K(k-30)N32, ..., KkN32,
K1N33, K2N33, ..., K32N33,
K1N34, K2N34, ..., K32N34,
...
K(k-31)Nn, K(k-30)Nn, ..., KkNn]
float16:
native layout: (N / 16, K / 32, 16, 32)
[K1N1, K2N1, ..., K32N1,
K1N2, K2N2, ..., K32N2,
...
K1N16, K2N16, ..., K32N16,
K33N1, K34N1, ..., K64N1,
K33N2, K34N2, ..., K64N2,
...
K(k-31)N16, K(k-30)N16, ..., KkN16,
K1N17, K2N17, ..., K32N17,
K1N18, K2N18, ..., K32N18,
...
K(k-31)Nn, K(k-30)Nn, ..., KkNn]
C shape: M x N
normal layout: (M, N)
[M1N1, M1N2, ..., M1Nn,
M2N1, M2N2, ..., M2Nn,
...
MmN1, MmN2, ..., MmNn]
perf layout: (N / 4, M, 4)
[N1M1, N2M1, ..., N4M1,
N5M2, N6M2, ..., N8M2,
...
N(n-3)Mm, N(n-2)Mm, ..., NnMm]
*/
int rknn_matmul_set_io_mem(rknn_matmul_ctx ctx, rknn_tensor_mem* mem, rknn_matmul_tensor_attr* attr);
/* rknn_matmul_set_core_mask
set rknn core mask.(only support rk3588 in current)
RKNN_NPU_CORE_AUTO: auto mode, default value
RKNN_NPU_CORE_0: core 0 mode
RKNN_NPU_CORE_1: core 1 mode
RKNN_NPU_CORE_2: core 2 mode
RKNN_NPU_CORE_0_1: combine core 0/1 mode
RKNN_NPU_CORE_0_1_2: combine core 0/1/2 mode
input:
rknn_matmul_ctx context the handle of context.
rknn_core_mask core_mask the core mask.
return:
int error code.
*/
int rknn_matmul_set_core_mask(rknn_matmul_ctx context, rknn_core_mask core_mask);
/* rknn_matmul_run
run the matmul in blocking mode
params:
rknn_matmul_ctx ctx the handle of context.
return:
int error code.
*/
int rknn_matmul_run(rknn_matmul_ctx ctx);
/* rknn_matmul_destroy
destroy the matmul context
params:
rknn_matmul_ctx ctx the handle of context.
return:
int error code.
*/
int rknn_matmul_destroy(rknn_matmul_ctx ctx);
#ifdef __cplusplus
} // extern "C"
#endif
#endif // _RKNN_MATMUL_API_H