6.6. 模型推理

C接口详细介绍请阅读《 BMRUNTIME开发参考手册 》。

​Python接口详细介绍请阅读《 sophon-sail使用手册 》。

​BMRuntime用于读取BMCompiler的编译输出(.bmodel)，驱动其在SOPHON 智能视觉深度学习处理器中执行。BMRuntime向用户提供了丰富的接口，便于用户移植算法，其软件架构如下:

../_images/1.png

BMRuntime实现了C/C++接口，SAIL模块基于对BMRuntime和BMLib的封装实现了Python接口。本章主要介绍C和Python常用接口，主要内容如下：

  • BMLib 接口：负责设备Handle的管理、内存管理、数据搬运、API的发送和同步、A53使能、设置智能视觉深度学习处理器工作频率等

  • BMRuntime的C语言接口

  • BMLib和BMRuntime的Python接口介绍

6.6.1. BMLib模块C接口介绍

BMLIB接口

  • 用于设备管理，不属于BMRuntime，但需要配合使用，所以先介绍。

    BMLIB接口是C语言接口，对应的头文件是bmlib_runtime.h，对应的lib库为libbmlib.so。

    BMLIB接口用于设备管理，包括设备内存的管理。

    BMLIB的接口很多，这里介绍应用程序通常需要用到的接口。

  • bm_dev_request

用于请求一个设备，得到设备句柄handle。其他设备接口，都需要指定这个设备句柄。 其中devid表示设备号，在PCIe模式下，存在多个设备时可以用于选择对应的设备；在SoC模式下，请指定为0。

 1/**
 2 * @name    bm_dev_request
 3 * @brief   To create a handle for the given device
 4 * @ingroup bmlib_runtime
 5 *
 6 * @param [out] handle  The created handle
 7 * @param [in]  devid   Specify on which device to create handle
 8 * @retval  BM_SUCCESS  Succeeds.
 9 *          Other code  Fails.
10 */
11bm_status_t bm_dev_request(bm_handle_t *handle, int devid);

  • bm_dev_free

用于释放一个设备。通常应用程序开始需要请求一个设备，退出前释放这个设备。

1/**
2 * @name    bm_dev_free
3 * @brief   To free a handle
4 * @param [in] handle  The handle to free
5 */
6void bm_dev_free(bm_handle_t handle);

6.6.2. BMRuntime模块C接口介绍

对应的头文件为bmruntime_interface.h，对应的lib库为libbmrt.so。

用户程序使用C接口时建议使用该接口，该接口支持多种shape的静态编译网络，支持动态编译网络。

  • bmrt_create

1/**
2 * @name    bmrt_create
3 * @brief   To create the bmruntime with bm_handle.
4 * This API creates the bmruntime. It returns a void* pointer which is the pointer
5 * of bmruntime. Device id is set when get bm_handle;
6 * @param [in] bm_handle     bm handle. It must be initialized by using bmlib.
7 * @retval void* the pointer of bmruntime
8 */
9void* bmrt_create(bm_handle_t bm_handle);

  • bmrt_destroy

1/**
2 * @name    bmrt_destroy
3 * @brief   To destroy the bmruntime pointer
4 * @ingroup bmruntime
5 * This API destroy the bmruntime.
6 * @param [in]     p_bmrt        Bmruntime that had been created
7 */
8void bmrt_destroy(void* p_bmrt);

  • bmrt_load_bmodel

加载bmodel文件，加载后bmruntime中就会存在若干网络的数据，后续可以对网络进行推理。

 1/**
 2 * @name    bmrt_load_bmodel
 3 * @brief   To load the bmodel which is created by BM compiler
 4 * This API is to load bmodel created by BM compiler.
 5 * After loading bmodel, we can run the inference of neuron network.
 6 * @param   [in]   p_bmrt        Bmruntime that had been created
 7 * @param   [in]   bmodel_path   Bmodel file directory.
 8 * @retval true    Load context sucess.
 9 * @retval false   Load context failed.
10 */
11bool bmrt_load_bmodel(void* p_bmrt, const char *bmodel_path);

  • bmrt_load_bmodel_data

加载bmodel，不同于bmrt_load_bmodel，它的bmodel数据存在内存中

1/*
2Parameters: [in] p_bmrt      - Bmruntime that had been created.
3            [in] bmodel_data - Bmodel data pointer to buffer.
4            [in] size        - Bmodel data size.
5Returns:    bool             - true: success; false: failed.
6*/
7bool bmrt_load_bmodel_data(void* p_bmrt, const void * bmodel_data, size_t size);

  • bmrt_get_network_info

bmrt_get_network_info根据网络名，得到某个网络的信息

 1/* bm_stage_info_t holds input shapes and output shapes;
 2every network can contain one or more stages */
 3typedef struct {
 4bm_shape_t* input_shapes;   /* input_shapes[0] / [1] / ... / [input_num-1] */
 5bm_shape_t* output_shapes;  /* output_shapes[0] / [1] / ... / [output_num-1] */
 6} bm_stage_info_t;
 7
 8/* bm_tensor_info_t holds all information of one net */
 9typedef struct {
10const char* name;              /* net name */
11bool is_dynamic;               /* dynamic or static */
12int input_num;                 /* number of inputs */
13char const** input_names;      /* input_names[0] / [1] / .../ [input_num-1] */
14bm_data_type_t* input_dtypes;  /* input_dtypes[0] / [1] / .../ [input_num-1] */
15float* input_scales;           /* input_scales[0] / [1] / .../ [input_num-1] */
16int output_num;                /* number of outputs */
17char const** output_names;     /* output_names[0] / [1] / .../ [output_num-1] */
18bm_data_type_t* output_dtypes; /* output_dtypes[0] / [1] / .../ [output_num-1] */
19float* output_scales;          /* output_scales[0] / [1] / .../ [output_num-1] */
20int stage_num;                 /* number of stages */
21bm_stage_info_t* stages;       /* stages[0] / [1] / ... / [stage_num-1] */
22} bm_net_info_t;

bm_net_info_t表示一个网络的全部信息，bm_stage_info_t表示该网络支持的不同的shape情况。

1/**
2 * @name    bmrt_get_network_info
3 * @brief   To get network info by net name
4 * @param [in]     p_bmrt         Bmruntime that had been created
5 * @param [in]     net_name       Network name
6 * @retval  bm_net_info_t*        Pointer to net info, needn't free by user; if net name not found, will return NULL.
7 */
8const bm_net_info_t* bmrt_get_network_info(void* p_bmrt, const char* net_name);

示例代码：

 1const char *model_name = "VGG_VOC0712_SSD_300X300_deploy"
 2const char **net_names = NULL;
 3bm_handle_t bm_handle;
 4bm_dev_request(&bm_handle, 0);
 5void * p_bmrt = bmrt_create(bm_handle);
 6bool ret = bmrt_load_bmodel(p_bmrt, bmodel.c_str());
 7std::string bmodel; //bmodel file
 8int net_num = bmrt_get_network_number(p_bmrt, model_name);
 9bmrt_get_network_names(p_bmrt, &net_names);
10for (int i=0; i<net_num; i++) {
11//do somthing here
12......
13}
14free(net_names);
15bmrt_destroy(p_bmrt);
16bm_dev_free(bm_handle);

  • bmrt_shape_count

接口声明如下：

1/*
2number of shape elements, shape should not be NULL and num_dims should not large than BM_MAX_DIMS_NUM
3*/
4uint64_t bmrt_shape_count(const bm_shape_t* shape);

可以得到shape的元素个数。

比如num_dims为4，则得到的个数为dims[0]*dims[1]*dims[2]*dims[3]

bm_shape_t 结构介绍：

1typedef struct {
2int num_dims;
3int dims[BM_MAX_DIMS_NUM];
4} bm_shape_t;

bm_shape_t表示tensor的shape，目前最大支持8维的tensor。其中num_dims为tensor的实际维度数，dims为各维度值，dims的各维度值从[0]开始，比如(n, c, h, w)四维分别对应(dims[0], dims[1], dims[2], dims[3])。

如果是常量shape，初始化参考如下：

1bm_shape_t shape = {4, {4,3,228,228}};
2bm_shape_t shape_array[2] = {
3{4, {4,3,28,28}}, // [0]
4{2, {2,4}}, // [1]
5}

  • bm_image_from_mat

 1//if use this function you need to open USE_OPENCV macro in include/bmruntime/bm_wrapper.hpp
 2/**
 3* @name    bm_image_from_mat
 4* @brief   Convert opencv Mat object to BMCV bm_image object
 5* @param [in]     in          OPENCV mat object
 6* @param [out]    out         BMCV bm_image object
 7* @retval true    Launch success.
 8* @retval false   Launch failed.
 9*/
10static inline bool bm_image_from_mat (cv::Mat &in, bm_image &out)

1//* @brief   Convert opencv multi Mat object to multi BMCV bm_image object
2static inline bool bm_image_from_mat (std::vector<cv::Mat> &in, std::vector<bm_image> &out)

  • bm_image_from_frame

 1/**
 2 * @name    bm_image_from_frame
 3 * @brief   Convert ffmpeg a avframe object to a BMCV bm_image object
 4 * @ingroup bmruntime
 5 *
 6 * @param [in]     bm_handle   the low level device handle
 7 * @param [in]     in          a read-only avframe
 8 * @param [out]    out         an uninitialized BMCV bm_image object
 9                     use bm_image_destroy function to free out parameter until                              you no longer useing it.
10 * @retval true    change success.
11 * @retval false   change failed.
12 */
13
14static inline bool bm_image_from_frame (bm_handle_t       &bm_handle,
15                                      AVFrame           &in,
16                                      bm_image          &out)

 1/**
 2 * @name    bm_image_from_frame
 3 * @brief   Convert ffmpeg avframe  to BMCV bm_image object
 4 * @ingroup bmruntime
 5 *
 6 * @param [in]     bm_handle   the low level device handle
 7 * @param [in]     in          a read-only ffmpeg avframe vector
 8 * @param [out]    out         an uninitialized BMCV bm_image vector
 9                   use bm_image_destroy function to free out parameter until                              you no longer useing it.
10 * @retval true    change success.
11 * @retval false   chaneg failed.
12 */
13static inline bool bm_image_from_frame (bm_handle_t                &bm_handle,
14                                      std::vector<AVFrame>       &in,
15                                      std::vector<bm_image>      &out)

  • bm_inference

 1//if use this function you need to open USE_OPENCV macro in include/bmruntime/bm_wrapper.hpp
 2/**
 3* @name    bm_inference
 4* @brief   A block inference wrapper call
 5* @ingroup bmruntime
 6*
 7* This API supports the neuron nework that is static-compiled or dynamic-compiled
 8* After calling this API, inference on TPU is launched. And the CPU
 9* program will be blocked.
10* This API support single input && single output, and multi thread safety
11*
12* @param [in]    p_bmrt         Bmruntime that had been created
13* @param [in]    input          bm_image of single-input data
14* @param [in]    output         Pointer of  single-output buffer
15* @param [in]    net_name       The name of the neuron network
16* @param [in]    input_shape    single-input shape
17*
18* @retval true    Launch success.
19* @retval false   Launch failed.
20*/
21static inline bool bm_inference (void           *p_bmrt,
22                                bm_image        *input,
23                                void           *output,
24                                bm_shape_t input_shape,
25                                const char   *net_name)

1// * This API support single input && multi output, and multi thread safety
2static inline bool bm_inference (void                       *p_bmrt,
3                                bm_image                    *input,
4                                std::vector<void*>         outputs,
5                                bm_shape_t             input_shape,
6                                const char               *net_name)

1// * This API support multiple inputs && multiple outputs, and multi thread safety
2static inline bool bm_inference (void                           *p_bmrt,
3                                std::vector<bm_image*>          inputs,
4                                std::vector<void*>             outputs,
5                                std::vector<bm_shape_t>   input_shapes,
6                                const char                   *net_name)

6.6.3. Python接口

本章节只简要介绍了 YOLOv5 用例中所用的接口函数。

更多接口定义请查阅《 sophon-sail使用手册 》。

  • Engine

1def __init__(tpu_id):
2""" Constructor does not load bmodel.
3Parameters
4---------
5tpu_id : int TPU ID. You can use bm-smi to see available IDs
6"""

  • load

1def load(bmodel_path):
2"""Load bmodel from file.
3Parameters
4---------
5bmodel_path : str Path to bmode
6"""

  • set_io_mode

1def set_io_mode(mode):
2""" Set IOMode for a graph.
3Parameters
4---------
5mode : sail.IOMode Specified io mode
6"""

  • get_graph_names

1def get_graph_names():
2""" Get all graph names in the loaded bmodels.
3Returns
4------
5graph_names : list Graph names list in loaded context
6"""

  • get_input_names

1def get_input_names(graph_name):
2""" Get all input tensor names of the specified graph.
3Parameters
4---------
5graph_name : str Specified graph name
6Returns
7------
8input_names : list All the input tensor names of the graph
9"""

  • get_output_names

1def get_output_names(graph_name):
2""" Get all output tensor names of the specified graph.
3Parameters
4---------
5graph_name : str Specified graph name
6Returns
7------
8input_names : list All the output tensor names of the graph
9"""

  • sail.IOMode

1# Input tensors are in system memory while output tensors are in device memory sail.IOMode.SYSI
2# Input tensors are in device memory while output tensors are in system memory.
3sail.IOMode.SYSO
4# Both input and output tensors are in system memory.
5sail.IOMode.SYSIO
6# Both input and output tensors are in device memory.
7ail.IOMode.DEVIO

  • sail.Tensor

 1def __init__(handle, shape, dtype, own_sys_data, own_dev_data):
 2""" Constructor allocates system memory and device memory of the tensor.
 3Parameters
 4---------
 5handle : sail.Handle Handle instance
 6shape : tuple Tensor shape
 7dytpe : sail.Dtype Data type
 8own_sys_data : bool Indicator of whether own system memory
 9own_dev_data : bool Indicator of whether own device memory
10"""

  • get_input_dtype

1def get_input_dtype(graph_name, tensor_name):
2""" Get scale of an input tensor. Only used for int8 models.
3Parameters
4---------
5graph_name : str The specified graph name tensor_name : str The specified output tensor name
6Returns
7------
8scale: sail.Dtype Data type of the input tensor
9"""

  • get_output_dtype

1def get_output_dtype(graph_name, tensor_name):
2""" Get the shape of an output tensor in a graph.
3Parameters
4---------
5graph_name : str The specified graph name tensor_name : str The specified output tensor name
6Returns
7------
8tensor_shape : list The shape of the tensor
9"""

  • process

1def process(graph_name, input_tensors, output_tensors):
2""" Inference with provided input and output tensors.
3Parameters
4---------
5graph_name : str The specified graph name
6input_tensors : dict {str : sail.Tensor} Input tensors managed by user
7output_tensors : dict {str : sail.Tensor} Output tensors managed by user
8"""

  • get_input_scale

1def get_input_scale(graph_name, tensor_name):
2""" Get scale of an input tensor. Only used for int8 models.
3Parameters
4---------
5graph_name : str The specified graph name tensor_name : str The specified output tensor name
6Returns
7------
8scale: float32 Scale of the input tensor
9"""

  • get_output_scale

 1def get_output_scale(graph_name, tensor_name)
 2""" Get scale of an output tensor. Only used for int8 models.
 3
 4Parameters
 5----------
 6graph_name : str
 7    The specified graph name
 8tensor_name : str
 9    The specified output tensor name
10
11Returns
12-------
13scale: float32
14    Scale of the output tensor
15"""

  • get_input_shape

 1def get_input_shape(graph_name, tensor_name):
 2""" Get the maximum dimension shape of an input tensor in a graph.
 3    There are cases that there are multiple input shapes in one input name,
 4    This API only returns the maximum dimension one for the memory allocation
 5    in order to get the best performance.
 6
 7Parameters
 8----------
 9graph_name : str
10    The specified graph name
11tensor_name : str
12    The specified input tensor name
13
14Returns
15-------
16tensor_shape : list
17    The maxmim dimension shape of the tensor
18"""

  • get_output_shape

 1def get_output_shape(graph_name, tensor_name):
 2""" Get the shape of an output tensor in a graph.
 3
 4Parameters
 5----------
 6graph_name : str
 7    The specified graph name
 8tensor_name : str
 9    The specified output tensor name
10
11Returns
12-------
13tensor_shape : list
14    The shape of the tensor
15"""