Model optimization method

A non-transitory computer-readable storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor on a first device, cause the processor to perform the steps of:

• performing training using a first training dataset to obtain a first model;
• transforming the first training dataset with a transformation function to obtain a transformed dataset, and generating a second training dataset based on the first training dataset and the transformed dataset, wherein the transformation function represents a processing capability of a second device to which a model is to be deployed with respect to data in the first training dataset;
• training the first model using the second training dataset to obtain an adjusted first model;
• performing performance tests on the first model and the adjusted first model using a test dataset to respectively obtain a first performance metric of the first model and a second performance metric of the adjusted first model, and calculating a performance metric difference between the first performance metric and the second performance metric;
• adjusting the second training dataset based on the performance metric difference to generate an adjusted second training dataset, and training the adjusted first model using the adjusted second training dataset to obtain a second model;
• and
• performing dynamic quantization on the second model to obtain an optimized model deployable to the second device.

A system comprising:

• a first device including:
• a memory storing instructions for an application;
• and
• a processor coupled to the memory that implements the application by performing the steps of:
• performing training using a first training dataset to obtain a first model;
• transforming the first training dataset with a transformation function to obtain a transformed dataset, and generating a second training dataset based on the first training dataset and the transformed dataset, wherein the transformation function represents a processing capability of a second device to which a model is to be deployed with respect to data in the first training dataset;
• training the first model using the second training dataset to obtain an adjusted first model;
• performing performance tests on the first model and the adjusted first model using a test dataset to respectively obtain a first performance metric of the first model and a second performance metric of the adjusted first model, and calculating a performance metric difference between the first performance metric and the second performance metric;
• adjusting the second training dataset based on the performance metric difference to generate an adjusted second training dataset, and training the adjusted first model using the adjusted second training dataset to obtain a second model;
• and
• performing dynamic quantization on the second model on the first device to obtain an optimized model deployable to the second device.