Inference Using the MindIR Model on Ascend 310 AI Processors

Linux Ascend Inference Application Beginner Intermediate Expert

Overview

Ascend 310 is a highly efficient and integrated AI processor oriented to edge scenarios. The Atlas 200 Developer Kit (Atlas 200 DK) is a developer board that uses the Atlas 200 AI accelerator module. Integrated with the HiSilicon Ascend 310 AI processor, the Atlas 200 allows data analysis, inference, and computing for various data such as images and videos, and can be widely used in scenarios such as intelligent surveillance, robots, drones, and video servers.

This tutorial describes how to use MindSpore to perform inference on the Atlas 200 DK based on the MindIR model file. The process is as follows:

1. Prepare the development environment, including creating an SD card for the Atlas 200 DK, configuring the Python environment, and updating the development software package.

2. Export the MindIR model file. The ResNet-50 model is used as an example.

3. Build the inference code to generate an executable main file.

4. Load the saved MindIR model, perform inference, and view the result.

You can obtain the complete executable sample code at https://gitee.com/mindspore/docs/tree/r1.2/tutorials/tutorial_code/ascend310_resnet50_preprocess_sample.

Preparing the Development Environment

Refer to Inference on the Ascend 310 AI Processor to install the device, and then refer to Installation Guide to install MindSpore.

Exporting the MindIR Model

Train the target network on the Ascend 910 AI Processor, save it as a checkpoint file, and export the model file in MindIR format through the network and checkpoint file. For details about the export process, see Export MindIR Model.

The resnet50_imagenet.mindir is a sample MindIR file exported using the ResNet-50 model.

Inference Directory Structure

Create a directory to store the inference code project, for example, /home/HwHiAiUser/Ascend/ascend-toolkit/20.0.RC1/acllib_linux.arm64/sample/acl_execute_model/ascend310_resnet50_preprocess_sample. The directory code can be obtained from the official website. The model directory stores the exported MindIR model files and the test_data directory stores the images to be classified. The directory structure of the inference code project is as follows:

└─ascend310_resnet50_preprocess_sample
    ├── CMakeLists.txt                    // Build script
    ├── README.md                         // Usage description
    ├── main.cc                           // Main function
    ├── model
    │   └── resnet50_imagenet.mindir      // MindIR model file
    └── test_data
        ├── ILSVRC2012_val_00002138.JPEG  // Input sample image 1
        ├── ILSVRC2012_val_00003014.JPEG  // Input sample image 2
        ├── ...                           // Input sample image n

Inference Code

Data-preprocessing by CPU operators

Inference sample code: https://gitee.com/mindspore/docs/blob/r1.2/tutorials/tutorial_code/ascend310_resnet50_preprocess_sample/main.cc .

Using namespace of mindspore and mindspore::dataset.

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

Set global context, device target is Ascend 310 and device id is 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);

Load mindir file:

// 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);

Get informance of this model:

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

Load image file:

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

Image preprocess(CPU operators):

// 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);

Execute the model:

// 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);

Print the result:

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

Data pre-processing by Ascend 310 operators

Dvpp module is a hardware decoder embedded in Ascend 310 AI chip which has a better performance on image processing compare with CPU operators. Several transforms applied on JPEG format image are supported.

Using namespace of mindspore and mindspore::dataset.

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

Set global context, device target is Ascend 310 and device id is 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);

Load image file:

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

Image preprocess(Ascend 310 operators):

// Create the CPU 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}));

Image preprocess (Ascend 310 operators, 130% performance increasing compare to CPU operators).

Explicitly specify the computing hardware as 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);

Load mindir file: Ascend 310 operators must bind with Aipp module, insert Aipp module for model graph compiling.

// 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);

Get input information of this model:

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

Execute the model:

// 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);

Print the result:

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

Introduce to Building Script

The building script is used to building applications: https://gitee.com/mindspore/docs/blob/r1.2/tutorials/tutorial_code/ascend310_resnet50_preprocess_sample/CMakeLists.txt.

Add head files to gcc search path:

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

Find the shared libraries in MindSpore:

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

Use the source files to generate the target executable file, and link the MindSpore libraries for the executable file:

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

Building Inference Code

Go to the project directory ascend310_resnet50_preprocess_sample and set the following environment variables:

# 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=${LOCAL_ASCEND}/ascend-toolkit/latest/acllib/lib64:${LOCAL_ASCEND}/ascend-toolkit/latest/atc/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}

# 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=${LOCAL_ASCEND}/ascend-toolkit/latest/atc/ccec_compiler/bin/:${PATH}                       # TBE operator compilation tool path
export PYTHONPATH=${TBE_IMPL_PATH}:${PYTHONPATH}                                                       # Python library that TBE implementation depends on

Run the cmake command, modify pip3 according to the actual situation:

cmake . -DMINDSPORE_PATH=`pip3 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`

Run the make command for building.

make

After building, the executable main file is generated in ascend310_resnet50_preprocess_sample.

Performing Inference and Viewing the Result

Log in to the Atlas 200 DK developer board, and create the model directory for storing the MindIR file resnet50_imagenet.mindir, for example, /home/HwHiAiUser/Ascend/ascend-toolkit/20.0.RC1/acllib_linux.arm64/sample/acl_execute_model/ascend310_resnet50_preprocess_sample/model. Create the test_data directory to store images, for example, /home/HwHiAiUser/Ascend/ascend-toolkit/20.0.RC1/acllib_linux.arm64/sample/acl_execute_model/ascend310_resnet50_preprocess_sample/test_data. Then, perform the inference.

./resnet50_sample

Inference is performed on all images stored in the test_data directory. For example, if there are 9 images whose label is 0 in the ImageNet2012 validation set, the inference result is as follows:

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