# Inference on the Ascend 910 AI processor
[](https://gitee.com/mindspore/docs/blob/r1.9/tutorials/experts/source_en/infer/ascend_910_mindir.md)
## Overview
Users can create C++ applications and call MindSpore C++ interface to inference MindIR models.
## Inference Directory Structure
Create a directory to store the inference code project, for example, `/home/HwHiAiUser/mindspore_sample/ascend910_resnet50_preprocess_sample`. The directory code can be obtained from the [official website](https://gitee.com/mindspore/docs/tree/r1.9/docs/sample_code/ascend910_resnet50_preprocess_sample). The `model` directory stores the exported `MindIR` [model files](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/sample_resources/ascend310_resnet50_preprocess_sample/resnet50_imagenet.mindir) and the `test_data` directory stores the images to be classified. The directory structure of the inference code project is as follows:
```text
└─ascend910_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
Inference sample code: [ascend310_resnet50_preprocess_sample](https://gitee.com/mindspore/docs/blob/r1.9/docs/sample_code/ascend310_resnet50_preprocess_sample/main.cc).
Using namespace of `mindspore` and `mindspore::dataset`.
```c++
namespace ms = mindspore;
namespace ds = mindspore::dataset;
```
Set global context, and device target is Ascend910 and Device ID is 0:
```c++
auto context = std::make_shared<ms::Context>();
auto ascend910_info = std::make_shared<ms::Ascend910DeviceInfo>();
ascend910_info->SetDeviceID(0);
context->MutableDeviceInfo().push_back(ascend910_info);
```
Load model file:
```c++
// 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 input information required for the model:
```c++
std::vector<ms::MSTensor> model_inputs = resnet50.GetInputs();
```
Load image file:
```c++
// Readfile is a function to read images
ms::MSTensor ReadFile(const std::string &file);
auto image = ReadFile(image_file);
```
Image preprocess:
```c++
// 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 inference:
```c++
// 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:
```c++
// Output the maximum probability to the screen
std::cout << "Image: " << image_file << " infer result: " << GetMax(outputs[0]) << std::endl;
```
## Introducing Building Script
The building script is used to building applications: [CMakeLists.txt](https://gitee.com/mindspore/docs/blob/r1.9/docs/sample_code/ascend910_resnet50_preprocess_sample/CMakeLists.txt).
Add the header file search path for the compiler:
```cmake
option(MINDSPORE_PATH "mindspore install path" "")
include_directories(${MINDSPORE_PATH})
include_directories(${MINDSPORE_PATH}/include)
```
Search for the required dynamic library in MindSpore:
```cmake
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:
```cmake
add_executable(resnet50_sample main.cc)
target_link_libraries(resnet50_sample ${MS_LIB} ${MD_LIB})
```
## Building Inference Code
Go to the project directory `ascend910_resnet50_preprocess_sample` and set the following environment variables:
```bash
# control log level. 0-DEBUG, 1-INFO, 2-WARNING, 3-ERROR, 4-CRITICAL, default level is WARNING.
export GLOG_v=2
# Conda environmental options
LOCAL_ASCEND=/usr/local/Ascend # the root directory of run package
# lib libraries that the run package depends on
export LD_LIBRARY_PATH=${LOCAL_ASCEND}/ascend-toolkit/latest/lib64:${LOCAL_ASCEND}/driver/lib64/common:${LOCAL_ASCEND}/driver/lib64/driver:${LOCAL_ASCEND}/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe/op_tiling:${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=${LOCAL_ASCEND}/ascend-toolkit/latest/compiler/ccec_compiler/bin/:${PATH} # TBE operator compilation tool path
export PYTHONPATH=${TBE_IMPL_PATH}:${PYTHONPATH} # Python library that TBE implementation depends on
```
Run the `cmake` command, and modify `pip3` according to the actual situation:
```bash
cmake . -DMINDSPORE_PATH=`pip3 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`
```
Run the `make` command for building.
```bash
make
```
After building, the executable file is generated in `ascend910_resnet50_preprocess_sample`.
## Performing Inference and Viewing the Result
Log in to the Ascend 910 server, and create the `model` directory for storing the MindIR file `resnet50_imagenet.mindir`, for example, `/home/HwHiAiUser/mindspore_sample/ascend910_resnet50_preprocess_sample/model`.
Create the `test_data` directory to store images, for example, `/home/HwHiAiUser/mindspore_sample/ascend910_resnet50_preprocess_sample/test_data`.
Then, perform the inference.
```bash
./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](http://image-net.org/download-images) validation set, the inference result is as follows:
```text
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
```