Getting Started

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This chapter introduces how to apply Mech-DLK SDK to achieve inference of the Defect Segmentation model exported from Mech-DLK.

Prerequisites

Inference Flow

inference flow

Function Description

In this section, we take the Defect Segmentation model exported from Mech-DLK as an example to show the functions you need to use when using Mech-DLK SDK for model inference.

Create an Input Image

MMindImage input;
createImage("path/to/image.png", &input);

Call the function createImage to create an input image.

Create an Inference Engine

Engine engine;
createPackInferEngine(&engine, "path/to/xxx.dlkpack", GpuDefault, 0);

Call the function createPackInferEngine to create an inference engine.

  • If NVIDIA discrete graphics cards are available on your device, you can set the inference backend, i.e., the third parameter in the function, to GpuDefault or GpuOptimization.

    • When the inference backend is set to GpuOptimization, you need to wait for 1 to 5 minutes for model optimization.

  • If NVIDIA discrete graphics cards are unavailable on your device, you can only set the inference backend to CPU.

  • In this function, the fourth parameter represents the ID of the used NVIDIA graphics cards, which is 0 when there is only one graphics card. When the inference backend is set to CPU, this parameter is invalid.

Deep Learning Engine Inference

infer(&engine, &input, 1);

Call the function infer for deep learning engine inference.

In this function, the parameter 1 denotes the number of images for inference, which should equal the number of images in input.

Obtain the Defect Segmentation Result

DefectAndEdgeResult* defectAndEdgeResult = NULL;
unsigned int resultNum = 0;
getDefectSegmentataionResult(&engine, 0, &defectAndEdgeResult, &resultNum);

Call the function getDefectSegmentataionResult to obtain the result of the defect segmentation model.

In this function, the second parameter 0 denotes the model index in the deep learning model inference package.

  • If the inference package is of a single model, the parameter can only be set to 0.

  • If the inference package is of cascaded models, the parameter should be set according to the order of modules in the model inference package.

Result Visualization

resultVisualization(&engine, &input, 1);
showImage(&input, "result");

Call the function resultVisualization to plot the model inference result on the image(s) in input.

In this function, the parameter 1 denotes the number of images for inference, which should equal the number of images in input.

Release Memory

releaseDefectSegmentationResult(&defectAndEdgeResult, resultNum);
releaseImage(&input);
releasePackInferEngine(&engine);

Release the memory of model inference results, input image(s), and the model engine to prevent memory leaks.

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