Detailed Explanation of Model Conversion and Preprocessing Technologies in HarmonyOS Next

This article aims to deeply explore the model conversion and preprocessing technologies in the Huawei HarmonyOS Next system (up to API 12 as of now), and summarize them based on practical development practices. It mainly serves as a vehicle for technical sharing and communication. There may be mistakes and omissions. Colleagues are welcome to put forward valuable opinions and questions so that we can make progress together. This article is original content, and any form of reprint must indicate the source and the original author.

I. Model Conversion Requirements and HarmonyOS Next Solutions

(1) Analysis of Necessity

In the development of smart applications for HarmonyOS Next, model conversion is a crucial task, and its necessity is reflected in multiple aspects.

Firstly, adapting to different hardware is one of the key requirements for model conversion. The HarmonyOS Next ecosystem encompasses various types of devices, which have significant differences in hardware architecture, computing power, storage resources, etc. For example, devices such as smartphones, smartwatches, and smart speakers have different hardware configurations. To enable a model trained on one device to run efficiently on other devices, model conversion is required. Take a deep learning model as an example. If a large neural network model trained on a server side is directly deployed on a smartwatch with limited resources, due to the limited computing power and storage resources of the watch, the model may not run properly or run extremely inefficiently. Through model conversion, the model can be optimized into a format and structure suitable for the smartwatch's hardware, enabling it to perform inference calculations quickly and accurately on the watch.

Secondly, improving the operation efficiency is also an important goal of model conversion. Different hardware platforms have different impacts on the operation efficiency of the model. For example, some devices may be equipped with a dedicated neural network processor (NPU), which is optimized for the calculation of deep learning models and can significantly improve the running speed of the model. Through model conversion, the model can be transformed into a format that can fully utilize the acceleration features of these hardware components, thereby significantly improving the operation efficiency of the model on specific devices. In addition, model conversion can also optimize the model, such as reducing the number of model parameters and optimizing the calculation process, further improving the running speed and resource utilization rate of the model.

(2) Introduction to HarmonyOS Next Model Conversion Tools

HarmonyOS Next provides a series of model conversion tools to meet different development needs. Among them, the OMG offline model conversion tool is an important tool.

The OMG offline model conversion tool has rich functional features. It supports multiple deep learning frameworks, such as TensorFlow, PyTorch, etc. This enables developers to conveniently convert models trained under different frameworks into a format that can be recognized and run by HarmonyOS Next devices. For example, for an image recognition model trained under the TensorFlow framework, using the OMG offline model conversion tool can convert it into a model format suitable for HarmonyOS Next mobile phones, thus enabling the deployment of the model on the mobile phone. The tool also provides a variety of parameter configuration options, allowing developers to finely adjust the conversion process according to specific needs. For example, the running mode of the model conversion can be set (such as generating a DaVinci model, converting the model to json, etc.). By converting the model to json format, developers can view the text format of the model structure, which is convenient for model analysis and debugging. In terms of model quantization, the OMG offline model conversion tool also provides corresponding functions. Through quantization, the storage size of the model can be reduced, the computational complexity can be lowered, and the operation efficiency of the model on devices with limited resources can be improved.

(3) Comparison of Different Model Conversion Methods

1. Using the Official Conversion Tool (such as the OMG Offline Model Conversion Tool) The advantage is that it has good compatibility with the HarmonyOS Next system and can make full use of the features and optimizations provided by the system. For example, it can automatically perform some optimization operations according to the hardware information of the target device, such as adaptation to a specific NPU. At the same time, the official tool usually provides relatively complete documentation and technical support, which is convenient for developers to use. However, its flexibility may be relatively limited, and it may not be able to directly meet some special needs or customized conversion operations. For example, if developers need to perform specific structural adjustments to the model or adopt non-standard quantization methods, the official tool may not provide direct support.
2. Method Based on Open-Source Conversion Libraries The method based on open-source conversion libraries (such as ONNX, etc.) has high flexibility, and developers can deeply customize the conversion process according to their own needs. Open-source conversion libraries usually support multiple deep learning frameworks and can conveniently achieve model conversion between different frameworks. For example, using ONNX can convert TensorFlow models and PyTorch models to each other, and then further convert them into a format that can be run on HarmonyOS Next. However, using open-source conversion libraries requires developers to have a deeper understanding of related technologies, and some compatibility issues may be encountered during the integration process. For example, the version update of the open-source conversion library may lead to incompatibility with the HarmonyOS Next system or other dependent libraries, requiring developers to spend more time on debugging and solving these problems.

II. Model Conversion Steps and Preprocessing Operations

(1) Detailed Explanation of Conversion Steps

1. Preparation Work Before performing model conversion, a series of preparation work needs to be completed. First is the environment setup to ensure that the necessary software and dependencies are installed in the system. For the OMG offline model conversion tool, it may be necessary to install the Java Runtime Environment (JRE) because the tool may be developed based on Java. At the same time, according to the requirements of the tool, other relevant dependent libraries or software packages may also need to be installed. For example, if the model conversion involves a specific deep learning framework, it is necessary to ensure that the corresponding framework has been correctly installed and the environment variables have been configured. When installing dependent libraries, pay attention to the compatibility of versions to avoid problems during the conversion process due to version mismatches.
2. Parameter Configuration Parameter configuration during the model conversion process is crucial. Taking the OMG offline model conversion tool as an example, multiple parameters need to be set according to the conversion requirements. For example, use the --mode parameter to specify the running mode. For example, 0 means generating a DaVinci model, 1 means converting the model to json, and 3 means only doing a pre-inspection, etc. When converting models from other frameworks into a format that can be run on HarmonyOS Next, the type of deep learning framework needs to be selected through the --framework parameter, such as 3 for TensorFlow, 5 for PyTorch or ONNX, etc. At the same time, the path of the original model file (--model parameter) and the output path of the converted model (--output parameter) need to be specified. For conversions involving model quantization, parameters related to quantization also need to be set, such as the path of the quantization configuration file for the calibration method (--cal_conf parameter). This file contains some key configuration information during the quantization process, such as the selection of the quantization algorithm and the setting of the quantization range. When configuring the parameters, carefully read the documentation of the tool to ensure that the parameter settings are correct. Otherwise, it may lead to conversion failure or the converted model not being able to be used properly.
3. Execute the Conversion After completing the preparation work and parameter configuration, the model conversion operation can be executed. Run the model conversion tool, and the tool will read the original model file according to the set parameters and perform a series of conversion operations, including the conversion of the model structure and the adjustment of parameters (such as quantization operations). During the conversion process, pay close attention to the log information output by the console and promptly discover and solve possible problems. For example, if an error message of data format mismatch appears, it may be due to the input and output format of the original model being inconsistent with the format expected by the conversion tool. It is necessary to check the model definition and parameter configuration and make corresponding adjustments. After the conversion is completed, the converted model file will be generated, which can be deployed and run on HarmonyOS Next devices.

(2) Elaboration on the Importance of Model Preprocessing and Code Examples (if applicable)

1. Elaboration on the Importance Model preprocessing plays a vital role in the entire process of model conversion and application. Data normalization is a common preprocessing operation that maps data to a specific interval or gives it specific statistical characteristics. For example, in image data processing, normalizing the pixel values of an image to the interval of 0 to 1 or -1 to 1 can make the model process the data more stably and efficiently during training and inference. Different features may have different dimensions and value ranges. If normalization is not performed, the model may pay excessive attention to features with larger values and ignore other important features. For example, in a dataset containing two features, age (with a possible value range of 0 to 100) and income (with a possible value range of 0 to 100,000), used for predicting consumer behavior, if normalization is not carried out, the value of the income feature is much larger than that of the age feature, and the model may consider income as the only important factor and ignore the impact of age on consumer behavior.

Cropping is also one of the important preprocessing steps. In image data, cropping off the irrelevant edge parts can reduce the amount of data and highlight the key areas of the image, improving the processing efficiency and accuracy of the model. For example, in a face recognition model, cropping off the background part of the image except for the face can reduce the interference of background information on the model, enabling the model to focus more on the learning and recognition of facial features. In addition, cropping can also adjust the image to the input size expected by the model to ensure that the model can process the data correctly.

1. Code Example (Taking Data Normalization as an Example) Suppose we use Python and the numpy library to perform data normalization operations (the following code is only an example, and in actual applications, it may need to be adjusted according to specific data and model requirements):

import numpy as np

# Suppose data is a two-dimensional array, where each row represents a sample and each column represents a feature
data = np.array([[10, 200], [20, 300], [30, 400], [40, 500]])

# Normalize the data to the interval of 0 to 1
min_vals = np.min(data, axis=0)
max_vals = np.max(data, axis=0)
normalized_data = (data - min_vals) / (max_vals - min_vals)

print(normalized_data)

In this example, first, the minimum and maximum values of each feature are calculated, and then the data is normalized so that the value of each feature is within the interval of 0 to 1. Such preprocessing operations can improve the processing effect of the model on the data and thus enhance the performance of the model.

(3) Analysis of Problems and Solutions

1. Data Format Mismatch Problem During the model conversion and preprocessing process, data format mismatch is one of the common problems. For example, the input data format of the original model may be different from the format expected by the conversion tool, or the format of the preprocessed dataset may be inconsistent with the input format required by the model. The solution is to carefully check the requirements of the data format to ensure the consistency of the data format during the model training, preprocessing, and conversion processes. If a format mismatch is found, corresponding format conversion operations need to be carried out. For example, if the model requires the input image format to be NHWC (number of batches, height, width, number of channels), but the preprocessed image format is NCHW (number of batches, number of channels, height, width), then the corresponding functions or tools need to be used to convert the image format to make it meet the requirements of the model.
2. Conversion Failure Problem Conversion failure may be caused by various reasons. Incorrect parameter configuration is one of the common reasons, such as incorrectly specifying the model file path, output path, framework type, and other parameters. The solution is to carefully check the parameter settings and ensure the correctness of the parameters according to the documentation of the tool. In addition, problems with the model itself may also lead to conversion failure, such as damaged model structures or unsupported operations. If it is suspected that the problem lies with the model itself, you can try to check and repair the model in the original deep learning framework, or retrain a usable model. In addition, compatibility problems of dependent libraries may also cause conversion failure. For example, the version of a certain dependent library is not compatible with the model conversion tool. In this case, it is necessary to check the version of the dependent library and install the appropriate version of the dependent library according to the requirements of the tool.
3. Resource Insufficiency Problem During the model conversion process, situations of resource insufficiency may be encountered, such as insufficient memory or insufficient disk space. If there is insufficient memory, it may cause the program to crash or run slowly during the conversion process. The solution is to optimize the use of system resources, close other unnecessary programs, and release memory resources. If possible, increase the memory capacity of the system. For the problem of insufficient disk space, it is necessary to clean up the disk space to ensure that there is enough space to store the files generated during the conversion process, such as temporary files and the converted model file. At the same time, when selecting the model conversion tool and configuring the parameters, some optimization strategies can also be considered, such as choosing a conversion mode or parameter settings that consume fewer resources to adapt to an environment with limited resources.

(3) Performance Evaluation and Optimization Strategies (Continued)

1. Performance Evaluation Indicators and Methods for the Model after Conversion After the model conversion is completed, it is necessary to evaluate the performance of the converted model to ensure that it meets the application requirements. The evaluation indicators mainly include the accuracy and operation efficiency of the model. The accuracy can be measured by the prediction accuracy rate on the test dataset, that is, input the test data into the converted model and calculate the proportion of the number of samples correctly predicted by the model to the total number of samples. For example, in an image classification model, use a test set containing images of different categories, and count the ratio of the number of correctly classified images to the total number of images by the model to obtain the accuracy rate of the model. The operation efficiency can be evaluated by the inference speed of the model, that is, measure the time required for the model to process a single sample or a batch of samples. A timer can be used to record the time interval from when the model receives the input data to when it outputs the prediction result, and the average value obtained through multiple tests can be used to obtain a more accurate inference speed.
2. Proposed Optimization Strategies For the performance optimization of the model after conversion, it can start from multiple aspects. Model compression is an effective optimization strategy, which improves the operation efficiency of the model by reducing the number of model parameters and the storage size. For example, adopt pruning technology to remove unimportant connections or neurons in the model and make the model more streamlined. During the pruning process, according to the structure of the model and the task requirements, select an appropriate pruning strategy and threshold to avoid a decline in model performance caused by excessive pruning. Quantization is also a commonly used optimization method. Converting the parameters in the model from high-precision data types (such as 32-bit floating-point numbers) to low-precision data types (such as 8-bit integers) can reduce the computational complexity and at the same time reduce the storage requirements of the model. When performing quantization, pay attention to selecting an appropriate quantization algorithm and parameters to ensure that the quantized model can still maintain high accuracy. In addition, the calculation graph of the model can also be optimized, such as merging some consecutive operations and eliminating redundant nodes, etc., to improve the calculation efficiency of the model. In practical applications, multiple optimization strategies can be combined, and according to the specific device performance and application requirements, the best optimization solution can be found. It is hoped that through the introduction of this article, everyone can have a deeper understanding of the model conversion and preprocessing technologies in HarmonyOS Next and can better apply these technologies in practical development, providing strong support for the development of smart applications. If you encounter other problems in the practice process, you are welcome to communicate and discuss together! Haha!