Ascend 310 AI处理器上使用MindIR模型进行推理

Linux Ascend 推理应用 初级 中级 高级

查看源文件

概述

Ascend 310是面向边缘场景的高能效高集成度AI处理器，本教程介绍如何在Ascend 310上使用MindSpore基于MindIR模型文件执行推理，主要包括以下流程：

  1. 导出MindIR模型文件，这里以ResNet-50模型为例。

  2. 编译推理代码，生成可执行main文件。

  3. 加载保存的MindIR模型，执行推理并查看结果。

开发环境准备

参考安装指导准备Ascend环境与MindSpore。

导出MindIR模型文件

在CPU/GPU/Ascend 910的机器上训练好目标网络，并保存为CheckPoint文件，通过网络和CheckPoint文件导出对应的MindIR格式模型文件，导出流程参见导出MindIR格式文件。

这里提供使用BatchSize为1的ResNet-50模型导出的示例MindIR文件resnet50_imagenet.mindir。

推理目录结构介绍

创建目录放置推理代码工程，例如/home/HwHiAiUser/Ascend/ascend-toolkit/20.0.RC1/acllib_linux.arm64/sample/acl_execute_model/ascend310_resnet50_preprocess_sample，可以从官网示例下载样例代码，model目录用于存放上述导出的MindIR模型文件，test_data目录用于存放待分类的图片，推理代码工程目录结构如下:

└─ascend310_resnet50_preprocess_sample
    ├── CMakeLists.txt                    // 构建脚本
    ├── README.md                         // 使用说明
    ├── main.cc                           // 主函数
    ├── model
    │   └── resnet50_imagenet.mindir      // MindIR模型文件
    └── test_data
        ├── ILSVRC2012_val_00002138.JPEG  // 输入样本图片1
        ├── ILSVRC2012_val_00003014.JPEG  // 输入样本图片2
        ├── ...                           // 输入样本图片n

推理代码介绍

使用CPU算子数据预处理

推理代码样例：https://gitee.com/mindspore/docs/blob/r1.3/docs/sample_code/ascend310_resnet50_preprocess_sample/main.cc 。

引用mindspore和mindspore::dataset的名字空间。

namespace ms = mindspore;
namespace ds = mindspore::dataset;

环境初始化，指定硬件为Ascend 310，DeviceID为0：

auto context = std::make_shared<ms::Context>();
auto ascend310_info = std::make_shared<ms::Ascend310DeviceInfo>();
ascend310_info->SetDeviceID(0);
context->MutableDeviceInfo().push_back(ascend310_info);

加载模型文件:

// Load MindIR model
ms::Graph graph;
ms::Status ret = ms::Serialization::Load(resnet_file, ms::ModelType::kMindIR, &graph);
// Build model with graph object
ms::Model resnet50;
ret = resnet50.Build(ms::GraphCell(graph), context);

获取模型所需输入信息：

std::vector<ms::MSTensor> model_inputs = resnet50.GetInputs();

加载图片文件:

// Readfile is a function to read images
ms::MSTensor ReadFile(const std::string &file);
auto image = ReadFile(image_file);

图片预处理（使用CPU算子）:

// Create the CPU operator provided by MindData to get the function object

// Decode the input to RGB format
std::shared_ptr<ds::TensorTransform> decode(new ds::vision::Decode());
// Resize the image to the given size
std::shared_ptr<ds::TensorTransform> resize(new ds::vision::Resize({256}));
// Normalize the input
std::shared_ptr<ds::TensorTransform> normalize(new ds::vision::Normalize(
    {0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}));
// Crop the input image at the center
std::shared_ptr<ds::TensorTransform> center_crop(new ds::vision::CenterCrop({224, 224}));
// shape (H, W, C) to shape (C, H, W)
std::shared_ptr<ds::TensorTransform> hwc2chw(new ds::vision::HWC2CHW());

// // Define a MindData preprocessor
ds::Execute preprocessor({decode, resize, normalize, center_crop, hwc2chw});

// Call the function object to get the processed image
ret = preprocessor(image, &image);

执行推理:

// Create outputs vector
std::vector<ms::MSTensor> outputs;
// Create inputs vector
std::vector<ms::MSTensor> inputs;
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
                    image.Data().get(), image.DataSize());
// Call the Predict function of Model for inference
ret = resnet50.Predict(inputs, &outputs);

获取推理结果:

// Output the maximum probability to the screen
std::cout << "Image: " << image_file << " infer result: " << GetMax(outputs[0]) << std::endl;

使用Ascend 310算子数据预处理

Dvpp模块为Ascend 310芯片内置硬件解码器，相较于CPU拥有对图形处理更强劲的性能。支持JPEG图片的解码缩放等基础操作。

引用mindspore和mindspore::dataset的名字空间。

namespace ms = mindspore;
namespace ds = mindspore::dataset;

环境初始化，指定硬件为Ascend 310，DeviceID为0：

auto context = std::make_shared<ms::Context>();
auto ascend310_info = std::make_shared<ms::Ascend310DeviceInfo>();
ascend310_info->SetDeviceID(0);
context->MutableDeviceInfo().push_back(ascend310_info);

加载图片文件:

// Readfile is a function to read images
ms::MSTensor ReadFile(const std::string &file);
auto image = ReadFile(image_file);

图片预处理（使用Ascend 310算子）:

// Create the Dvpp operator provided by MindData to get the function object

// Decode the input to YUV420 format
std::shared_ptr<ds::TensorTransform> decode(new ds::vision::Decode());
// Resize the image to the given size
std::shared_ptr<ds::TensorTransform> resize(new ds::vision::Resize({256}));
// Normalize the input
std::shared_ptr<ds::TensorTransform> normalize(new ds::vision::Normalize(
    {0.485 * 255, 0.456 * 255, 0.406 * 255}, {0.229 * 255, 0.224 * 255, 0.225 * 255}));
// Crop the input image at the center
std::shared_ptr<ds::TensorTransform> center_crop(new ds::vision::CenterCrop({224, 224}));

图片预处理（使用Ascend 310算子， 性能为CPU算子的2.3倍），需显式指定计算硬件为Ascend 310。

// Define a MindData preprocessor, set deviceType = kAscend310， device id = 0
ds::Execute preprocessor({decode, resize, center_crop, normalize}, MapTargetDevice::kAscend310, 0);

// Call the function object to get the processed image
ret = preprocessor(image, &image);

加载模型文件: 若使用Ascend 310算子，则需要为模型插入Aipp算子。

// Load MindIR model
ms::Graph graph;
ms::Status ret = ms::Serialization::Load(resnet_file, ms::ModelType::kMindIR, &graph);
// Build model with graph object
ascend310_info->SetInsertOpConfigPath(preprocessor.AippCfgGenerator());
ms::Model resnet50;
ret = resnet50.Build(ms::GraphCell(graph), context);

获取模型所需输入信息：

std::vector<ms::MSTensor> model_inputs = resnet50.GetInputs();

执行推理:

// Create outputs vector
std::vector<ms::MSTensor> outputs;
// Create inputs vector
std::vector<ms::MSTensor> inputs;
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
                    image.Data().get(), image.DataSize());
// Call the Predict function of Model for inference
ret = resnet50.Predict(inputs, &outputs);

获取推理结果:

// Output the maximum probability to the screen
std::cout << "Image: " << image_file << " infer result: " << GetMax(outputs[0]) << std::endl;

构建脚本介绍

构建脚本用于构建用户程序，样例来自于：https://gitee.com/mindspore/docs/blob/r1.3/docs/sample_code/ascend310_resnet50_preprocess_sample/CMakeLists.txt 。

为编译器添加头文件搜索路径：

option(MINDSPORE_PATH "mindspore install path" "")
include_directories(${MINDSPORE_PATH})
include_directories(${MINDSPORE_PATH}/include)

在MindSpore中查找所需动态库：

find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)

使用指定的源文件生成目标可执行文件，并为目标文件链接MindSpore库：

add_executable(resnet50_sample main.cc)
target_link_libraries(resnet50_sample ${MS_LIB} ${MD_LIB})

编译推理代码

进入工程目录ascend310_resnet50_preprocess_sample，设置如下环境变量：

# control log level. 0-DEBUG, 1-INFO, 2-WARNING, 3-ERROR, default level is WARNING.
export GLOG_v=2

# Conda environmental options
LOCAL_ASCEND=/usr/local/Ascend # the root directory of run package

# lib libraries that the run package depends on
export LD_LIBRARY_PATH=${LOCAL_ASCEND}/ascend-toolkit/latest/fwkacllib/lib64:${LOCAL_ASCEND}/driver/lib64:${LOCAL_ASCEND}/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe/op_tiling:${LD_LIBRARY_PATH}

# lib libraries that the mindspore depends on, modify "pip3" according to the actual situation
export LD_LIBRARY_PATH=`pip3 show mindspore-ascend | grep Location | awk '{print $2"/mindspore/lib"}' | xargs realpath`:${LD_LIBRARY_PATH}
# if MindSpore is installed by binary, run "export LD_LIBRARY_PATH=path-to-your-custom-dir:${LD_LIBRARY_PATH}"

# Environment variables that must be configured
export TBE_IMPL_PATH=${LOCAL_ASCEND}/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe            # TBE operator implementation tool path
export ASCEND_OPP_PATH=${LOCAL_ASCEND}/ascend-toolkit/latest/opp                                       # OPP path
export PATH=${LOCAL_ASCEND}/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin/:${PATH}                 # TBE operator compilation tool path
export PYTHONPATH=${TBE_IMPL_PATH}:${PYTHONPATH}                                                       # Python library that TBE implementation depends on

执行cmake命令，其中pip3需要按照实际情况修改：

cmake . -DMINDSPORE_PATH=`pip3 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`
# if MindSpore is installed by binary, run "cmake . -DMINDSPORE_PATH=path-to-your-custom-dir"

再执行make命令编译即可。

make

编译完成后，在ascend310_resnet50_preprocess_sample下会生成可执行main文件。

执行推理并查看结果

登录Ascend 310环境，创建model目录放置MindIR文件resnet50_imagenet.mindir，例如/home/HwHiAiUser/Ascend/ascend-toolkit/20.0.RC1/acllib_linux.arm64/sample/acl_execute_model/ascend310_resnet50_preprocess_sample/model。 创建test_data目录放置图片，例如/home/HwHiAiUser/Ascend/ascend-toolkit/20.0.RC1/acllib_linux.arm64/sample/acl_execute_model/ascend310_resnet50_preprocess_sample/test_data。 就可以开始执行推理了:

./resnet50_sample

执行后，会对test_data目录下放置的所有图片进行推理，比如放置了9张ImageNet2012验证集中label为0的图片，可以看到推理结果如下。

Image: ./test_data/ILSVRC2012_val_00002138.JPEG infer result: 0
Image: ./test_data/ILSVRC2012_val_00003014.JPEG infer result: 0
Image: ./test_data/ILSVRC2012_val_00006697.JPEG infer result: 0
Image: ./test_data/ILSVRC2012_val_00007197.JPEG infer result: 0
Image: ./test_data/ILSVRC2012_val_00009111.JPEG infer result: 0
Image: ./test_data/ILSVRC2012_val_00009191.JPEG infer result: 0
Image: ./test_data/ILSVRC2012_val_00009346.JPEG infer result: 0
Image: ./test_data/ILSVRC2012_val_00009379.JPEG infer result: 0
Image: ./test_data/ILSVRC2012_val_00009396.JPEG infer result: 0