CN112506483B - Data augmentation method, device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a data augmentation method, a device, electronic equipment and a storage medium, wherein the corresponding general augmentation method can be designed according to each product requirement of service data, and then when the service data with more complex data types is processed, the target augmentation method corresponding to the data types of the service data is executed, and the execution of the general augmentation method corresponding to the product requirement is acquired, so that the integral data augmentation logic is not required to be designed together with the combination types of the data types and the product requirement. When a plurality of business data have the same product demands, a general augmentation method is adopted when the data are processed, so that the writing of a plurality of repeated logic codes can be reduced, the general augmentation method can be suitable for processing business data of different data types, and the universality is strong.
Description
Technical Field
The present invention relates to the field of deep learning technologies, and in particular, to a data augmentation method, a data augmentation device, an electronic device, and a storage medium.
Background
Data augmentation is one of the techniques commonly used in deep learning, and is mainly used for adding training data sets, so that the data sets are diversified as much as possible, and the trained model has stronger generalization capability. In the current data augmentation method, different data types have different type IDs (Identity document, identity numbers) or type names, and each type ID or type name is respectively bound with its corresponding data augmentation logic, so when data is obtained, the correct data augmentation logic can be obtained according to the type ID of the data or the type name to which the data belongs, and then the data is processed according to the data augmentation logic.
Such a data augmentation scheme as described above may be applicable to data of a simple type, however, in current deep learning, the type of one data is generally complex, and in the case of the complex type, such a data augmentation scheme may cause a problem of redundancy of logic codes. For example, when data has a data type, but the data type also has a product requirement to which the data type belongs, the data can be regarded as complex type data formed by combining the data type and the requirement, if the data type and the requirement are too much, the combination mode is too many, the technician needs to design the corresponding data augmentation logic for different combination modes, thus obviously causing numerous and redundancy of the data augmentation logic codes, and each data augmentation logic can only be realized for one type or one combination mode and lacks versatility.
Disclosure of Invention
The invention provides a data augmentation method, a device, electronic equipment and a storage medium, which are used for solving the problems that code redundancy and augmentation logic are not universal easily caused under the condition that the data types are complex in the current data augmentation mode.
In a first aspect, the present invention provides a data augmentation method comprising:
acquiring the data type and the product requirement of service data; the product requirement is used for representing the effect or function which can be realized by the business data;
respectively determining a target augmentation method corresponding to the data type and a general augmentation method corresponding to the product demand by utilizing the data type and the product demand; the target augmentation method is used for representing specific data augmentation logic designed for the data category; the general augmentation method is used for representing data augmentation logic which is designed for the product requirement and can be universally used for various data types;
after the target augmentation method is performed, the generic augmentation method is performed.
With reference to the first aspect, in an implementation manner of the first aspect, the step of obtaining a data type and a product requirement of the service data includes:
acquiring a service name and a service scene to which service data belong;
and determining the product requirement corresponding to the service data by utilizing the service name and the service scene.
With reference to the first aspect, in an implementation manner of the first aspect, before the acquiring the data type and the product requirement of the service data, the method further includes:
acquiring all product requirements;
generating respective corresponding general augmentation methods for different product requirements;
and storing the general augmentation methods corresponding to different product requirements in a configuration file.
With reference to the first aspect, in an implementation manner of the first aspect, after performing the target augmentation method, the step of performing the generic augmentation method includes:
executing the target augmentation method;
and under the condition that the general augmentation method corresponding to the product requirement exists in the configuration file, continuing to execute the general augmentation method.
With reference to the first aspect, in an implementation manner of the first aspect, before the acquiring the data type and the product requirement of the service data, the method further includes:
acquiring all data types in all service data;
for each data class, a respective corresponding specific data augmentation logic is generated.
In a second aspect, the present invention provides a data augmentation apparatus comprising:
the information acquisition module is used for acquiring the data types and the product requirements of the service data; the product requirement is used for representing the effect or function which can be realized by the business data;
the method determining module is used for respectively determining a target augmentation method corresponding to the data type and a general augmentation method corresponding to the product demand by utilizing the data type and the product demand; the target augmentation method is used for representing specific data augmentation logic designed for the data category; the general augmentation method is used for representing data augmentation logic which is designed for the product requirement and can be universally used for various data types;
and the data augmentation module is used for executing the general augmentation method after executing the target augmentation method.
With reference to the second aspect, in an implementation manner of the second aspect, the information obtaining module includes:
the information acquisition unit is used for acquiring service names and service scenes to which the service data belong;
and the demand determining unit is used for determining the product demand corresponding to the service data by utilizing the service name and the service scene.
With reference to the second aspect, in an implementation manner of the second aspect, the apparatus further includes:
the demand type acquisition module is used for acquiring all product demands;
the first generation module is used for generating a corresponding general augmentation method according to different product requirements;
and the method configuration module is used for storing the general augmentation methods corresponding to different product requirements in the configuration file.
With reference to the second aspect, in an implementation manner of the second aspect, the data augmentation module includes:
a first augmentation unit for performing the target augmentation method;
and the second augmentation unit is used for continuously executing the general augmentation method under the condition that the general augmentation method corresponding to the product requirement exists in the configuration file.
With reference to the second aspect, in an implementation manner of the second aspect, the apparatus further includes:
the data type acquisition module is used for acquiring all data types in all service data;
and the second generation module is used for generating specific data augmentation logic corresponding to each data type.
In a third aspect, the present invention provides an electronic device, comprising:
a memory for storing program instructions;
and the processor is used for calling and executing the program instructions in the memory so as to realize the data augmentation method of the first aspect.
In a fourth aspect, the present invention provides a storage medium having a computer program stored therein, the data augmentation device performing the data augmentation method of the first aspect when the computer program is executed by at least one processor of the data augmentation device.
According to the technical scheme, the data augmentation method, the device, the electronic equipment and the storage medium can design the corresponding general augmentation method according to each product requirement of service data, and then acquire the general augmentation method corresponding to the product requirement to execute except the target augmentation method corresponding to the data type of the service data when the service data with more complex data types is processed, so that the integral data augmentation logic is not required to be designed together for the combination type of the data types and the product requirements. When a plurality of business data have the same product demands, a general augmentation method is adopted when the data are processed, so that the writing of a plurality of repeated logic codes can be reduced, the general augmentation method can be suitable for processing business data of different data types, and the universality is strong.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flow chart of a data augmentation method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a data augmentation process according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a data augmentation logic according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a second data augmentation logic according to an embodiment of the present invention;
FIG. 5 is a flow chart of a method of determining product demand according to an embodiment of the present invention;
FIG. 6 is a flow chart illustrating a method of generating a generic augmentation method according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating a second data augmentation process according to an embodiment of the present invention;
FIG. 8 is a block diagram illustrating a data amplification apparatus according to an embodiment of the present invention;
fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purposes of making the objects and embodiments of the present invention more apparent, an exemplary embodiment of the present invention will be described in detail below with reference to the accompanying drawings in which exemplary embodiments of the present invention are illustrated, it being apparent that the exemplary embodiments described are only some, but not all, of the embodiments of the present invention.
It should be noted that the brief description of the terminology in the present invention is for the purpose of facilitating understanding of the embodiments described below only and is not intended to limit the embodiments of the present invention. Unless otherwise indicated, these terms should be construed in their ordinary and customary meaning.
Data augmentation is one of the techniques commonly used in deep learning, and is mainly used for adding training data sets, so that the data sets are diversified as much as possible, and the trained model has stronger generalization capability. In the current data augmentation method, different data types have different type IDs (Identity document, identity numbers) or type names, and each type ID or type name is respectively bound with its corresponding data augmentation logic, so when data is obtained, the correct data augmentation logic can be obtained according to the type ID of the data or the type name to which the data belongs, and then the data is processed according to the data augmentation logic.
Such a data augmentation scheme as described above may be applicable to data of a simple type, however, in current deep learning, the type of one data is generally complex, and in the case of the complex type, such a data augmentation scheme may cause a problem of redundancy of logic codes. For example, when data has a data type, but the data type also has a product requirement to which the data type belongs, the data can be regarded as complex type data formed by combining the data type and the requirement, if the data type and the requirement are too much, the combination mode is too many, the technician needs to design the corresponding data augmentation logic for different combination modes, thus obviously causing numerous and redundancy of the data augmentation logic codes, and each data augmentation logic can only be realized for one type or one combination mode and lacks versatility.
Based on the above, the embodiments of the present invention provide a data augmentation method, apparatus, electronic device, and storage medium, which can design a corresponding general augmentation method for each product requirement of service data, so that writing of a plurality of repeated logic codes can be reduced, and the designed general augmentation method can be applied to processing of service data of different data types, and has strong versatility.
Fig. 1 is a flowchart of a data augmentation method according to an embodiment of the present invention. As shown in fig. 1, the data augmentation method provided by the embodiment of the present invention includes:
step S101, obtaining the data type and product requirement of the service data.
In general, service data is data transmitted from a service upstream, a service itself is determined, a scene to which the service belongs is also determined, so that the data itself has a determined service name, a scene to which the service belongs, and the like, and the service data also has a data type such as image data, text data, and the like.
The product requirements corresponding to the service data can be specifically analyzed through the service names and the service scenes. In the embodiment of the invention, the product requirement is the function or function which can be realized by the service data. Only if the product requirement is determined, specific operation can be performed on the business data according to the product requirement, so that corresponding actions or functions can be realized.
Step S102, respectively determining a target augmentation method corresponding to the data type and a general augmentation method corresponding to the product demand by utilizing the data type and the product demand.
In the embodiment of the present invention, the target augmentation method is used to represent specific data augmentation logic designed for the data types, that is, each data type has its own specific data augmentation logic, for example, the image data has a specific set of data augmentation logic a, the text data has a specific set of data augmentation logic B, etc. The general augmentation method is used to represent data augmentation logic designed for product requirements and capable of being universally used for various data types, namely one general augmentation logic is corresponding to one product requirement, but various business data can be corresponding to one product requirement, for example, the data types between image data and text data are different, but all belong to the same product requirement, and then the same general augmentation method, namely the data augmentation logic C, can be used for the image data and the text data.
Step S103, after the target augmentation method is performed, a general augmentation method is performed.
In the embodiment of the present invention, if the service data has a complex type structure, that is, has a data type and also has a specific product requirement, it is generally necessary to execute the target augmentation method corresponding to the data type first, and then execute the general augmentation method corresponding to the product requirement based on the target augmentation method. Taking the content of the above embodiment as an example, when the service data is image data and text data and both belong to the same product requirement, the data augmentation logic a may be executed first for processing the image data, then the data augmentation logic C may be executed, and the data augmentation logic B may be executed first for processing the text data, then the data augmentation logic C may be executed.
Fig. 2 is a schematic diagram of a data augmentation process according to an embodiment of the present invention.
The content of the foregoing embodiments of the present invention may be described in a more specific manner, as shown in fig. 2, the data augmentation method in the embodiment of the present invention may first obtain service data from the service upstream, then obtain the data type and the product requirement of the service data, then obtain the target augmentation method corresponding to the data type and the general augmentation method corresponding to the product requirement, respectively, and execute the target augmentation method first and then execute the general augmentation method.
FIG. 3 is a schematic diagram illustrating a data augmentation logic according to an embodiment of the present invention.
As shown in fig. 3, the service data 1 is image data and has a product requirement c, the service data 2 is text data and has a product requirement c, and then in the current data augmentation method, a specific data augmentation logic E needs to be written for the service data 1 of the combination of image + requirement c, and a specific data augmentation logic F needs to be written for the service data 2 of the combination of text + requirement c. The service data 3 is image data and has a product requirement d, the service data 4 is text data and has a product requirement d, and in the current data augmentation method, a specific data augmentation logic G needs to be written for the service data 3 combined by the image + the requirement d, and a specific data augmentation logic H needs to be written for the service data 4 combined by the text + the requirement d. If any combination is a new combination, which has not been previously presented, then in data augmentation, new data augmentation logic needs to be written for each new category of combined business data, and each new category combination needs to be written once. In a practical application scenario, the scheme of the embodiment of the invention can relate to a large amount of service data, and the more the types of service data are, the more new types of combinations are, so that the more new data augmentation logic is written for the service data, obviously, some unnecessary code redundancy situations can occur, one data augmentation logic cannot be suitable for a plurality of types of combinations, and the universality is poor.
FIG. 4 is a schematic diagram illustrating a second data augmentation logic according to an embodiment of the present invention.
As shown in fig. 4, with the data augmentation method in the embodiment of the present invention, a data augmentation logic a may be written for the business data of the type image, a data augmentation logic B may be written for the business data of the type text, and a corresponding general augmentation method may be designed for each product requirement of the business data, such as writing a data augmentation logic C for the product requirement C, and a data augmentation logic D for the product requirement D. When processing service data, the data type and the product requirement are divided, so that when the service data have the same product requirement, a general data augmentation can be adopted, as shown in fig. 4, the service data 1 is divided into image data and product requirement c, the service data 2 is divided into text data and product requirement c, the service data 3 is divided into image data and product requirement d, and the service data 4 is divided into text data and product requirement d. Then the data augmentation logic C corresponding to the product requirement C may be used for both the service data 1 and the service data 2, and the data augmentation logic D corresponding to the product requirement D may be used for both the service data 3 and the service data 4. In this way, even if the types of combinations are increased, the types of data and the product demands thereof are only required to be written in advance with corresponding data augmentation logic, and when the data augmentation logic is reappeared, new data augmentation logic does not need to be written again, and writing can be directly called. Therefore, the embodiment of the invention does not need to design specific data augmentation logic aiming at the combination of each data type and requirement, so that the writing of a plurality of repeated logic codes can be reduced, and the designed general augmentation method can be suitable for processing business data of different data types and has strong universality.
FIG. 5 is a flow chart of a method for determining product demand according to an embodiment of the present invention.
As described in the foregoing embodiments, the product requirements corresponding to the service data may be specifically analyzed through the service names and the service scenarios. Furthermore, in some embodiments, as shown in fig. 5, the method in the embodiment of the present invention may further include the following steps:
step S201, obtaining a service name and a service scene to which service data belong; step S202, determining the product requirement corresponding to the service data by using the service name and the service scene.
FIG. 6 is a flow chart illustrating a method of generating a generic augmentation method according to an embodiment of the present invention.
In order to rapidly amplify service data, a general amplification method is usually designed in advance, and when the service data needs to be processed, the pre-stored general amplification method can be directly obtained according to the product requirement. Furthermore, in some embodiments, as shown in fig. 6, the method in the embodiment of the present invention may further include the following steps:
step S301, obtaining all product requirements on each service line; step S302, generating respective corresponding general augmentation methods according to different product requirements; or step S302 may also write their corresponding general augmentation logic for different product requirements; step S303, each general augmentation method is stored in a configuration file so as to be called when processing service data.
In addition, in the configuration file, different identity marks can be distributed for different product demands, and each identity mark is respectively associated with a general augmentation method corresponding to each product demand, so that when service data is processed, the product demands of the service data are determined, and the corresponding general augmentation method can be obtained from the configuration file.
Fig. 7 is a schematic diagram of a second data augmentation process according to an embodiment of the present invention.
In practical situations, not all business data need to be subjected to the general augmentation process, i.e. the general data augmentation may not be performed for the business data belonging to certain product requirements. As described in the foregoing embodiment, the configuration file has a general augmentation method corresponding to all product requirements on the service line, where the service line refers to all services involved in the actual application scenario of the embodiment of the present invention, and when the product requirements of the service data do not belong to the service applied in the embodiment of the present invention, the general data augmentation may not be performed again for the product requirements of the service data, and the general augmentation method corresponding to the product requirements may not be provided in the configuration file.
Furthermore, in some embodiments, as shown in fig. 7, after obtaining the product requirement of the service data, it may also be detected whether there is a general augmentation method corresponding to the product requirement in the configuration file, if not, it is indicated that the general augmentation method is not required to be executed any more, and only a specific data augmentation method corresponding to the data type of the service data, that is, the target augmentation method in the embodiment of the present invention is executed. When the general augmentation method corresponding to the product requirement exists in the configuration file, the product requirement is described as belonging to the service related to the embodiment of the present invention, and then the general augmentation method corresponding to the product requirement needs to be continuously executed after the target augmentation method is executed.
The scheme of the embodiment of the invention can also predefine corresponding specific data augmentation logic aiming at different data types, for example, the image data and the text data in the previous embodiment can predefine corresponding data augmentation logic A and data augmentation logic B for the image data and the text data respectively. In practical applications, the embodiments of the present invention may relate to a plurality of service lines, and then, in some embodiments, multiple data types may be generally used, and in some embodiments, it is necessary to acquire all data types of all service data that may be related to the embodiments of the present invention as much as possible, and further, generate specific data augmentation logic corresponding to each data type for each data type, so that when the data augmentation method in the embodiments of the present invention is implemented later, the target augmentation method corresponding to the data type may be directly and quickly acquired.
As can be seen from the foregoing, the embodiments of the present invention provide a data augmentation method, which can design a corresponding general augmentation method for each product requirement of service data, and then, when service data with a relatively complex data type is processed, obtain, in addition to a target augmentation method corresponding to the data type of the service data itself, a general augmentation method corresponding to the product requirement for execution, so that it is not necessary to design an overall data augmentation logic together with the combination type of the data type and the product requirement. When a plurality of business data have the same product requirement, a general augmentation method is adopted when the data are processed. Therefore, the data augmentation method in the embodiment of the invention can reduce the writing of a plurality of repeated logic codes related to the product requirement. And the general augmentation method corresponding to the product demand can be suitable for processing the business data of different data types, as long as the business data has the corresponding product demand, so the data augmentation method in the embodiment of the invention has stronger universality.
In addition, the data augmentation method in the embodiment of the invention reduces the time for writing the logic codes by technicians while reducing the writing of a plurality of repeated logic codes, improves the code development efficiency of the technicians in the aspect of deep learning, and can also reduce the investment of excessive development cost.
Fig. 8 is a block diagram of a data amplifying apparatus according to an embodiment of the present invention. As shown in fig. 8, the apparatus may include:
an information obtaining module 801, configured to obtain a data type and a product requirement of service data; the product requirement is used for representing the effect or function which can be realized by the business data; a method determining module 802, configured to determine, by using the data type and the product requirement, a target augmentation method corresponding to the data type and a general augmentation method corresponding to the product requirement, respectively; the target augmentation method is used for representing specific data augmentation logic designed for the data category; the general augmentation method is used for representing data augmentation logic which is designed for the product requirement and can be universally used for various data types; the data augmentation module 803 is configured to execute the general augmentation method after executing the target augmentation method.
In some embodiments, the information acquisition module 801 includes: the information acquisition unit is used for acquiring service names and service scenes to which the service data belong; and the demand determining unit is used for determining the product demand corresponding to the service data by utilizing the service name and the service scene.
In some embodiments, the apparatus further comprises: the demand type acquisition module is used for acquiring all product demands; the first generation module is used for generating a corresponding general augmentation method according to different product requirements; and the method configuration module is used for storing the general augmentation methods corresponding to different product requirements in the configuration file.
In some embodiments, the data augmentation module 803 comprises: a first augmentation unit for performing the target augmentation method; and the second augmentation unit is used for continuously executing the general augmentation method under the condition that the general augmentation method corresponding to the product requirement exists in the configuration file.
In some embodiments, the apparatus further comprises: the data type acquisition module is used for acquiring all data types in all service data; and the second generation module is used for generating specific data augmentation logic corresponding to each data type.
Fig. 9 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention. As shown in fig. 9, the present invention further provides an electronic device, including: a memory 901 for storing program instructions; a processor 902 for calling and executing program instructions in the memory to implement the data augmentation method described in the above embodiment. Reference may be made in particular to the relevant description of the previous embodiments.
In embodiments of the invention, processor 902 and memory 901 may be connected via a bus or other means. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application specific integrated circuit, or one or more integrated circuits configured to implement embodiments of the present invention. The memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, a hard disk, or a solid state disk.
The embodiment of the invention also provides a storage medium, in which a computer program is stored, and when at least one processor of the data augmentation device executes the computer program, the data augmentation device executes the data augmentation method described in the above embodiment.
The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random-access memory (random access memory, RAM), or the like.
It will be apparent to those skilled in the art that the techniques of embodiments of the present invention may be implemented in software plus a necessary general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in essence or what contributes to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
The same or similar parts between the various embodiments in this specification are referred to each other. In particular, for the service building apparatus and the service loading apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description in the method embodiments for the matters.
The embodiments of the present invention described above do not limit the scope of the present invention.
Claims (12)
1. A method of data augmentation comprising:
acquiring the data type and the product requirement of service data; the product requirement is used for representing the effect or function which can be realized by the business data;
respectively determining a target augmentation method corresponding to the data type and a general augmentation method corresponding to the product demand by utilizing the data type and the product demand; the target augmentation method is used for representing specific data augmentation logic designed for the data category; the general augmentation method is used for representing data augmentation logic which is designed for the product requirement and can be universally used for various data types;
after the target augmentation method is performed, the generic augmentation method is performed.
2. The method of claim 1, wherein the step of obtaining the data type and the product requirement of the business data comprises:
acquiring a service name and a service scene to which service data belong;
and determining the product requirement corresponding to the service data by utilizing the service name and the service scene.
3. The method of claim 1, further comprising, prior to said obtaining the data type and product requirements of the business data:
acquiring all product requirements;
generating respective corresponding general augmentation methods for different product requirements;
and storing the general augmentation methods corresponding to different product requirements in a configuration file.
4. A method according to claim 3, wherein said step of performing said generic augmentation method after performing said target augmentation method comprises:
executing the target augmentation method;
and under the condition that the general augmentation method corresponding to the product requirement exists in the configuration file, continuing to execute the general augmentation method.
5. The method of claim 1, further comprising, prior to said obtaining the data type and product requirements of the business data:
acquiring all data types in all service data;
for each data class, a respective corresponding specific data augmentation logic is generated.
6. A data augmentation apparatus comprising:
the information acquisition module is used for acquiring the data types and the product requirements of the service data; the product requirement is used for representing the effect or function which can be realized by the business data;
the method determining module is used for respectively determining a target augmentation method corresponding to the data type and a general augmentation method corresponding to the product demand by utilizing the data type and the product demand; the target augmentation method is used for representing specific data augmentation logic designed for the data category; the general augmentation method is used for representing data augmentation logic which is designed for the product requirement and can be universally used for various data types;
and the data augmentation module is used for executing the general augmentation method after executing the target augmentation method.
7. The apparatus of claim 6, wherein the information acquisition module comprises:
the information acquisition unit is used for acquiring service names and service scenes to which the service data belong;
and the demand determining unit is used for determining the product demand corresponding to the service data by utilizing the service name and the service scene.
8. The apparatus of claim 6, wherein the apparatus further comprises:
the demand type acquisition module is used for acquiring all product demands;
the first generation module is used for generating a corresponding general augmentation method according to different product requirements;
and the method configuration module is used for storing the general augmentation methods corresponding to different product requirements in the configuration file.
9. The apparatus of claim 8, wherein the data augmentation module comprises:
a first augmentation unit for performing the target augmentation method;
and the second augmentation unit is used for continuously executing the general augmentation method under the condition that the general augmentation method corresponding to the product requirement exists in the configuration file.
10. The apparatus of claim 6, wherein the apparatus further comprises:
the data type acquisition module is used for acquiring all data types in all service data;
and the second generation module is used for generating specific data augmentation logic corresponding to each data type.
11. An electronic device, comprising:
a memory for storing program instructions;
a processor for invoking and executing program instructions in the memory to implement the data augmentation method of any of claims 1-5.
12. A storage medium having stored therein a computer program which, when executed by at least one processor of a data augmentation device, performs the data augmentation method of any of claims 1-5.
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