Inference on the Ascend 910 AI processor

Linux Ascend Inference Application Beginner Intermediate Expert

Inference Using a Checkpoint File with Single Device

Use the model.eval interface for model validation.

1.1 Local Storage

When the pre-trained models are saved in local, the steps of performing inference on validation dataset are as follows: firstly creating a model, then loading the model and parameters using load_checkpoint and load_param_into_net in mindspore.train.serialization module, and finally performing inference on the validation dataset once being created. The method of processing the validation dataset is the same as that of the training dataset.

network = LeNet5(cfg.num_classes)
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})

print("============== Starting Testing ==============")
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(network, param_dict)
dataset = create_dataset(os.path.join(args.data_path, "test"),
                         cfg.batch_size,
                         1)
acc = model.eval(dataset, dataset_sink_mode=args.dataset_sink_mode)
print("============== {} ==============".format(acc))

In the preceding information:
model.eval is an API for model validation. For details about the API, see https://www.mindspore.cn/doc/api_python/en/r1.1/mindspore/mindspore.html#mindspore.Model.eval.

Inference sample code: https://gitee.com/mindspore/mindspore/blob/r1.1/model_zoo/official/cv/lenet/eval.py.

1.2 Remote Storage

When the pre-trained models are saved remotely, the steps of performing inference on the validation dataset are as follows: firstly determining which model to be used, then loading the model and parameters using mindspore_hub.load, and finally performing inference on the validation dataset once being created. The method of processing the validation dataset is the same as that of the training dataset.

model_uid = "mindspore/ascend/0.7/googlenet_v1_cifar10"  # using GoogleNet as an example.
network = mindspore_hub.load(model_uid, num_classes=10)
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})

print("============== Starting Testing ==============")
dataset = create_dataset(os.path.join(args.data_path, "test"),
                         cfg.batch_size,
                         1)
acc = model.eval(dataset, dataset_sink_mode=args.dataset_sink_mode)
print("============== {} ==============".format(acc))

In the preceding information:

mindpsore_hub.load is an API for loading model parameters. Please check the details in https://www.mindspore.cn/doc/api_python/en/r1.1/mindspore_hub/mindspore_hub.html#module-mindspore_hub.

Use the model.predict API to perform inference.
```
model.predict(input_data)
```
In the preceding information:
model.predict is an API for inference. For details about the API, see https://www.mindspore.cn/doc/api_python/en/r1.1/mindspore/mindspore.html#mindspore.Model.predict.

Distributed Inference With Multi Devices

Distributed inference means use multiple devices for prediction. If data parallel or integrated save is used in training, the method of distributed inference is same with the above description. It is noted that each device should load one same checkpoint file.

This tutorial would focus on the process that the model slices are saved on each device in the distributed training process, and the model is reloaded according to the predication strategy in the inference stage. In view of the problem that there are too many parameters in the super large scale neural network model, the model can not be fully loaded into a single device for inference, so multiple devices can be used for distributed inference.

Distributed inference sample code:

https://gitee.com/mindspore/docs/tree/r1.1/tutorials/tutorial_code/distributed_inference

The process of distributed inference is as follows:

Execute training, generate the checkpoint file and the model strategy file.
- The distributed training tutorial and sample code can be referred to the link: https://www.mindspore.cn/tutorial/training/zh-CN/r1.1/advanced_use/distributed_training_ascend.html.
- In the distributed Inference scenario, during the training phase, the integrated_save of CheckpointConfig interface should be set to False, which means that each device only saves the slice of model instead of the full model.
- parallel_mode of set_auto_parallel_context interface should be set to auto_parallel or semi_auto_parallel.
- In addition, you need to specify strategy_ckpt_save_file to indicate the path of the strategy file.
Set context and infer predication strategy according to the predication data.
```
context.set_auto_parallel_context(full_batch=True, parallel_mode='semi_auto_parallel', strategy_ckpt_load_file='./train_strategy.ckpt')
network = Net()
model = Model(network)
predict_data = create_predict_data()
predict_strategy = model.infer_predict_layout(predict_data)
```
In the preceding information:
- full_batch: whether to load the dataset in full or not. When True, it indicates full load, and data of each device is the same. It must be set to True in this scenario.
- parallel_mode: parallel mode, it must be auto_parallel or semi_auto_parallel.
- strategy_ckpt_load_file: file path of the strategy generated in the training phase, which must be set in the distributed inference scenario.
- create_predict_data: user-defined interface that returns predication data whose type is Tensor.
- infer_predict_layout: generates predication strategy based on predication data.
Load checkpoint files, and load the corresponding model slice into each device based on the predication strategy.
```
ckpt_file_list = create_ckpt_file_list()
load_distributed_checkpoint(network, ckpt_file_list, predict_strategy)
```
In the preceding information:
- create_ckpt_file_list：user-defined interface that returns a list of checkpoint file path in order of rank id.
- load_distributed_checkpoint：merges model slices, then splits it according to the predication strategy, and loads it into the network.
The load_distributed_checkpoint interface supports that predict_strategy is None, which is single device inference, and the process is different from distributed inference. The detailed usage can be referred to the link: https://www.mindspore.cn/doc/api_python/zh-CN/r1.1/mindspore/mindspore.html#mindspore.load_distributed_checkpoint.
Execute inference.
```
model.predict(predict_data)
```

Use C++ Interface to Load a MindIR File for Inferencing

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. 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:

└─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: https://gitee.com/mindspore/docs/blob/r1.1/tutorials/tutorial_code/ascend310_resnet50_preprocess_sample/main.cc .

Set global context, device target is Ascend910 and evice id is 0:

ms::GlobalContext::SetGlobalDeviceTarget(ms::kDeviceTypeAscend910);
ms::GlobalContext::SetGlobalDeviceID(0);

Load mindir file:

// Load MindIR model
auto graph =ms::Serialization::LoadModel(resnet_file, ms::ModelType::kMindIR);
// Build model with graph object
ms::Model resnet50((ms::GraphCell(graph)));
ms::Status ret = resnet50.Build({});

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:

// Create the CPU operator provided by MindData to get the function object
ms::dataset::Execute preprocessor({ms::dataset::vision::Decode(),  // Decode the input to RGB format
                                   ms::dataset::vision::Resize({256}),  // Resize the image to the given size
                                   ms::dataset::vision::Normalize({0.485 * 255, 0.456 * 255, 0.406 * 255},
                                                                  {0.229 * 255, 0.224 * 255, 0.225 * 255}),  // Normalize the input
                                   ms::dataset::vision::CenterCrop({224, 224}),  // Crop the input image at the center
                                   ms::dataset::vision::HWC2CHW(),  // shape (H, W, C) to shape(C, H, W)
                                  });
// 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;

Introduce to Building Script

The building script is used to building applications: https://gitee.com/mindspore/docs/blob/r1.1/tutorials/tutorial_code/ascend910_resnet50_preprocess_sample/CMakeLists.txt.

Since MindSpore uses the old C++ ABI, applications must be the same with MindSpore, otherwise the building will fail.

add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
set(CMAKE_CXX_STANDARD 17)

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 ascend910_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=${LOCAL_ASCEND}/ascend-toolkit/latest/fwkacllib/lib64:${LOCAL_ASCEND}/driver/lib64/common:${LOCAL_ASCEND}/driver/lib64/driver:${LOCAL_ASCEND}/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=${LOCAL_ASCEND}/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin/:${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 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.

./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