Neural network model reasoning method and device, electronic equipment and readable medium

Abstract

The invention provides a neural network model reasoning method and device, electronic equipment and a readable medium, and relates to the technical field of neural networks. The method comprises the following steps of preprocessing a to-be-identified picture by adopting an assembly line method; loading a pre-trained neural network model into a neural network reasoning acceleration engine; and reasoning the preprocessed to-be-identified picture by adopting the loaded neural network reasoning acceleration engine to obtain a reasoning result. Picture preprocessing is subjected to parallel processing through an assembly line method, CPU resources are fully utilized, the reasoning performance is improved, a neural network reasoning acceleration engine is adopted for reasoning the acceleration engine, and the reasoning speed of a neural network model is increased.

Description

technical field [0001] The invention relates to the technical field of neural networks, in particular to a neural network model reasoning method, device, electronic equipment and readable medium. Background technique [0002] In the field of home decoration design, ResNet-50 is currently used to infer images based on the deep learning framework PyTorch. As the number of inference images increases, the inference time is also longer. The average inference time for each image reaches 18ms, and the inference efficiency is low. . Therefore, there is an urgent need to improve the inference performance of neural networks. Contents of the invention [0003] The purpose of the present invention is to provide a neural network model reasoning method, device, electronic equipment and readable medium, which can effectively reduce the reasoning time of pictures and improve reasoning performance. [0004] In the first aspect, a neural network model reasoning method provided by an embod...

AI Technical Summary

Problems solved by technology

[0002] In the field of home decoration design, ResNet-50 is currently used to infer images based on the deep learning framework PyTorch. As the number of inference images increases, the inference time is also longer. The average inference time for each image reaches 18ms, and the inference efficiency is low.

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