Image data processing method and device
An image data and processing method technology, applied in the field of image processing, can solve problems such as poor flexibility and limited use of Transformer modules, and achieve the effect of improving flexibility
Pending Publication Date: 2022-04-05
GUANGZHOU XIAOPENG CONNECTIVITY TECH CO LTD
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[0004] However, some chips on the market only support convolution calculations, so Transformer modules that require matrix multiplication operations cannot be deployed on these chips, which limits the use of Transformer modules and is less flexible.
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[0032] Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of this application to those skilled in the art.
[0033] The terminology used in this application is for the purpose of describing particular embodiments only, and is not intended to limit the application. As used in this application and the appended claims, the singular forms "a", "the", and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any and all possible ...
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The invention relates to an image data processing method and device. The method comprises the following steps: acquiring a first image matrix Am * p and a second image matrix Bp * n; determining a first feature map corresponding to the first image matrix Am * p; x convolution kernels corresponding to the second image matrix Bp * n are determined, the size of each convolution kernel is f * f * p, f is an integer larger than 1, X is an integer larger than or equal to 1, and each convolution kernel comprises one or more columns of elements in the second image matrix Bp * n; and carrying out convolution on the first feature map and the X convolution kernels to obtain a second feature map, the second feature map corresponding to a third image matrix, and the third image matrix being a result obtained by carrying out matrix multiplication on a first image matrix Am * p and a second image matrix Bp * n. According to the scheme provided by the invention, the flexibility of the Transform module can be improved.
Description
technical field [0001] The present application relates to the technical field of image processing, in particular to an image data processing method and device. Background technique [0002] The current mainstream machine translation is mainly based on neural network machine translation. This type of method is an "encoder-decoder" (encoder-decoder) architecture system. The encoder encodes the source language sequence, extracts information, and then The translator converts the information into the target language and completes the language translation process. The deep self-attention transform (Transformer) model based on the "encoder-decoder" architecture design has become the mainstream model in the field of machine translation due to its superior performance, and has had a huge impact in the field of deep learning. [0003] In the neural network with Transformer as the main module, there are two two-dimensional data tensors for matrix multiplication operations. In some sol...
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IPC IPC(8): G06V10/80G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06V10/82G06N3/04G06N3/08G06V10/80
Inventor 胡宇姬彬斐刘嘉超刘兰个川
Owner GUANGZHOU XIAOPENG CONNECTIVITY TECH CO LTD