CN114153540A - Pre-training model issuing method and device, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure relates to a method and a device for issuing a pre-training model, electronic equipment and a storage medium, wherein the method comprises the following steps: receiving a pre-training model uploaded by a user, and displaying a model information input interface to the user; receiving model information of the pre-training model input by a user; checking the model information according to a preset standard; and issuing the pre-training model under the condition that the model information meets the standard. The embodiment of the disclosure can improve the usability and convenience of the issued pre-training model.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for issuing a pre-training model, an electronic device, and a storage medium.
Background
With the continuous development of information technology, artificial intelligence technology is more and more popular, and model training is more and more emphasized as an important component of the artificial intelligence technology. In the model training process, a pre-training model can be obtained through pre-training, and in the pre-training process, the model is trained on a large amount of general linguistic data to learn general language knowledge, so that the pre-training model is obtained. And then, aiming at different tasks, carrying out transfer training on the pre-training model in a targeted manner to obtain a model suitable for the target task.
In the related art, pre-training models obtained by pre-training of different users do not have uniform specifications and standards, so that the use of the pre-training models by other users is inconvenient, and the usability and convenience of the pre-training models are low.
Disclosure of Invention
The present disclosure provides a technical scheme for issuing a pre-training model.
According to an aspect of the present disclosure, a method for issuing a pre-training model is provided, including:
receiving a pre-training model uploaded by a user, and displaying a model information input interface to the user;
receiving model information of the pre-training model input by a user;
checking the model information according to a preset standard;
and issuing the pre-training model under the condition that the model information meets the standard.
In a possible implementation manner, the receiving a pre-training model uploaded by a user includes: receiving a pre-training model uploaded to a background management platform by a user; the issuing the pre-training model comprises: and issuing the pre-training model to an open platform.
In one possible implementation, the model information includes at least one of:
the method comprises the following steps of model name, training task category of the model, warehouse address stored by model training codes, business item to which the model belongs, size of storage space occupied by the model, configuration file of the model, structure file of the model and training performance index of the model.
In one possible implementation, the preset criterion includes at least one of:
the model information comprises all necessary information;
a repository address of a model training code store in the model information can be accessed;
the configuration file in the model information and the structure file of the model can be normally visualized;
the training performance index of the pre-training model is normal.
In one possible implementation, the method further includes:
receiving an update model which is uploaded by a user and used for updating the published pre-training model, and displaying a model information input interface to the user;
receiving model information of the updated model input by a user;
verifying the model information of the updated model according to a preset standard;
and issuing the updated model under the condition that the model information of the updated model conforms to the standard.
In one possible implementation, the method further includes:
comparing the updated model with the pre-training model to determine the updated information between the updated model and the pre-training model;
and generating a version updating description of the pre-training model according to the updating information.
In one possible implementation, the states of the pre-trained model include at least one of:
an unpublished state, a published state, an updated state, and an offline state;
operations supported by the pre-training model for the unpublished state include: publishing, editing, checking details and deleting;
operations supported by the pre-trained model of the published state include: editing, checking details and canceling release;
operations supported by the pre-trained model of the updated state include: publishing, editing, checking details and canceling publishing;
operations supported by the pre-trained model of the offline state include: and (5) deleting.
According to an aspect of the present disclosure, there is provided a pre-training model issuing apparatus including:
the first uploading module is used for receiving the pre-training model uploaded by the user and displaying a model information input interface to the user;
the first information receiving module is used for receiving model information of the pre-training model input by a user;
the first checking module is used for checking the model information according to a preset standard;
and the first issuing module is used for issuing the pre-training model under the condition that the model information meets the standard.
In a possible implementation manner, the first uploading module is configured to receive a pre-training model uploaded to a background management platform by a user; the first publishing module is used for publishing the pre-training model to a public platform.
In one possible implementation, the model information includes at least one of:
the method comprises the following steps of model name, training task category of the model, warehouse address stored by model training codes, business item to which the model belongs, size of storage space occupied by the model, configuration file of the model, structure file of the model and training performance index of the model.
In one possible implementation, the preset criterion includes at least one of:
the model information comprises all necessary information;
a repository address of a model training code store in the model information can be accessed;
the configuration file in the model information and the structure file of the model can be normally visualized;
the training performance index of the pre-training model is normal.
In one possible implementation, the apparatus further includes:
the second uploading module is used for receiving an updating model uploaded by a user and used for updating the published pre-training model and displaying a model information input interface to the user;
the second information receiving module is used for receiving the model information of the updated model input by the user;
the second checking module is used for checking the model information of the updated model according to a preset standard;
and the second issuing module is used for issuing the updated model under the condition that the model information of the updated model meets the standard.
In one possible implementation, the apparatus further includes:
the comparison module is used for comparing the updated model with the pre-training model and determining the updated information between the updated model and the pre-training model;
and the updating description generation module is used for generating the version updating description of the pre-training model according to the updating information.
In one possible implementation, the states of the pre-trained model include at least one of:
an unpublished state, a published state, an updated state, and an offline state;
operations supported by the pre-training model for the unpublished state include: publishing, editing, checking details and deleting;
operations supported by the pre-trained model of the published state include: editing, checking details and canceling release;
operations supported by the pre-trained model of the updated state include: publishing, editing, checking details and canceling publishing;
operations supported by the pre-trained model of the offline state include: and (5) deleting.
According to an aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
According to an aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.
In the embodiment of the disclosure, a model information input interface is displayed to a user by receiving a pre-training model uploaded by the user; receiving model information of the pre-training model input by a user; checking the model information according to a preset standard; and issuing the pre-training model under the condition that the model information meets the standard. Therefore, a standardized pre-training model issuing flow is provided, model information is checked through a preset standard, the issued pre-training model meets the preset standard, adaptation cost caused by inconsistency of the model information is reduced, and usability and convenience of the issued pre-training model are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.
FIG. 1 shows a flow diagram of a pre-training model publishing method according to an embodiment of the disclosure.
FIG. 2 shows a schematic diagram of a model information input interface, according to an embodiment of the present disclosure.
FIG. 3 illustrates a diagram of relationships between states of a pre-trained model according to an embodiment of the present disclosure.
FIG. 4 shows a block diagram of a pre-training model issuing apparatus according to an embodiment of the present disclosure.
Fig. 5 shows a block diagram of an electronic device in accordance with an embodiment of the disclosure.
Fig. 6 illustrates a block diagram of an electronic device in accordance with an embodiment of the disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
In the related technology, inside and outside a company, different project groups and different service lines have different definitions on models, which may result in low utilization rate of pre-training models; meanwhile, there is no uniform management standard for the pre-training model, which results in low quality and usability of the pre-training model.
The method for issuing the pre-training model provided by the embodiment of the disclosure performs unified and standardized centralized management on the pre-training model, and a user can download and call the issued pre-training model for testing and tuning.
In the embodiment of the disclosure, a model information input interface is displayed to a user by receiving a pre-training model uploaded by the user; receiving model information of the pre-training model input by a user; checking the model information according to a preset standard; and issuing the pre-training model under the condition that the model information meets the standard. Therefore, a standardized pre-training model issuing flow is provided, model information is checked through a preset standard, the issued pre-training model meets the preset standard, adaptation cost caused by inconsistency of the model information is reduced, and usability and convenience of the issued pre-training model are improved.
In a possible implementation manner, the execution subject of the pre-training model issuing method may be a pre-training model management platform, and the platform may be run in an electronic device such as a terminal device or a server. The method for issuing the pre-training model may also be performed by an electronic device such as a terminal device or a server, where the terminal device may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, or the like, and the method may be implemented by a processor calling a computer-readable instruction stored in a memory.
For convenience of description, in one or more embodiments of the present specification, an execution subject of the pre-training model issuing method may be a pre-training model management platform, and hereinafter, an implementation of the method is described by taking the execution subject as the pre-training model management platform as an example. It is understood that the implementation subject of the method is only an exemplary illustration of the pre-training model management platform, and should not be construed as a limitation of the method.
Fig. 1 shows a flowchart of a pre-training model issuing method according to an embodiment of the present disclosure, and as shown in fig. 1, the pre-training model issuing method includes:
in step S11, receiving a pre-training model uploaded by a user, and displaying a model information input interface to the user;
the user can upload the pre-training model in the pre-training model management platform, and the pre-training model uploaded by the user can be stored in the memory so as to be released later.
The pre-training model uploaded by the user may be a model with any function, for example, the model may be a model for performing image recognition, a model for performing voice recognition, or a model for performing character recognition.
The model information input interface is used for a user to input model information of the pre-training model, where the model information includes information for describing the pre-training model, and reference may be made to possible implementation manners provided by the present disclosure, which is not described herein again.
And after the user clicks the uploading model button, a page of the uploading model appears for the user to upload the pre-training model and input the model information.
In step S12, receiving model information of the pre-training model input by a user;
after a model information input interface is displayed for a user, the user can input model information of a pre-training model in the interface and submit the model information to a pre-training model management platform, and the management platform can receive the model information of the pre-training model input by the user.
In step S13, the model information is verified according to a preset standard;
the preset standard may be a standard for standardizing the model information, and specific implementation of the preset standard may refer to possible implementation manners provided in the present disclosure, which are not described herein in detail.
In the process of checking, whether each piece of model information of the pre-training model meets the preset standard or not can be checked one by one, and under the condition that one piece of model information does not meet the preset mark, a notice that the model information cannot be checked is sent to a user, and a modification interface of the model information is provided for the user, so that the user can modify the pre-training model.
In step S14, the pre-trained model is released if the model information meets the criteria.
The user can fill in the version number of the pre-training model to be released during releasing, the model is disclosed after the releasing is confirmed, and the user can download, test and tune the pre-training model after the pre-training model is released.
In one example, publishing the pre-trained model may be publishing the pre-trained model to a platform accessible by a specified user, and in another example, publishing the pre-trained model may be setting the pre-trained model to a state accessible by the specified user, where the specified user may be a portion of the user or may be a state accessible by any user.
In the embodiment of the disclosure, a model information input interface is displayed to a user by receiving a pre-training model uploaded by the user; receiving model information of the pre-training model input by a user; checking the model information according to a preset standard; and issuing the pre-training model under the condition that the model information meets the standard. Therefore, a standardized pre-training model issuing flow is provided, model information is checked through a preset standard, the issued pre-training model meets the preset standard, adaptation cost caused by inconsistency of the model information is reduced, and usability and convenience of the issued pre-training model are improved.
In one possible implementation, the model information includes at least one of: the method comprises the following steps of model name, training task category of the model, warehouse address stored by model training codes, business item to which the model belongs, size of storage space occupied by the model, configuration file of the model, structure file of the model and training performance index of the model.
Please refer to fig. 2, which is a schematic diagram of a model information input interface according to the present disclosure.
Wherein, the model name (name) is used for naming the pre-training model, and the model name is a mandatory item.
A training task class (task) of the model, a class to which a training task for characterizing the model belongs, in one example, the class may be, for example, a detection task class, which is a class to which a model for performing a target detection task belongs, such as a model for detecting a face in an image, or a model for detecting a cat in an image, and so on; in another example, the class may be, for example, a classification task class, which is a class to which a model that performs a classification task belongs, e.g., a model used to classify images, and so on.
The stored warehouse address (git _ repo) of the model training code is used for indicating the storage position of a model training program in the network, and the model training program is a program used for training a pre-training model, and the program trains the pre-training model through training samples.
The business item (tag) to which the model belongs may be a certain business item in the enterprise, that is, to which business item in the enterprise the pre-trained model belongs.
The size of the memory space occupied by the model (model structure) for characterizing the memory space of the memory occupied by the model, in one example, the size of the memory space occupied by the model may be a specific value of the occupied memory space, for example: 5G; in another example, the size of the memory space occupied by the model may be used as a word that roughly represents the size of the memory space, e.g., may be small, medium, large, and so on.
The configuration file (config) of the model contains the configuration of the model parameters.
The structure files (weights) of the model contain parameters for characterizing the structure of the model, such as weight parameters of nodes in the model.
The training performance index (metric) of the model is used to characterize the performance of the model on a certain training set, and may be, for example, the accuracy of the model on each sample set, and so on.
In addition, the model information may also include other additional information, which is not described herein.
The three fields of the warehouse address stored by the model training code, the configuration file of the model and the structure file of the model can uniquely define a pre-training model, so that the three fields are not allowed to be modified when the model information is changed.
In the embodiment of the disclosure, the model information of the pre-training model is unified, so that a user of the pre-training model can better adapt to the model, and especially for the interior of a company, the adaptation cost caused by the inconsistency of the model information in the interior of the company is reduced.
In one possible implementation, the preset criterion includes at least one of: the model information comprises all necessary information; a repository address of a model training code store in the model information can be accessed; the configuration file in the model information and the structure file of the model can be normally visualized; the training performance index of the pre-training model is normal.
In this implementation, the required information in the model information may be specified, and in one example, the required information may be a model name, a training task type of the model, a warehouse address where a model training code is stored, a business item to which the model belongs, a configuration file of the model, and a structure file of the model.
In addition, the access detection can be carried out on the warehouse address stored by the model training code in the model information to determine whether the warehouse address can be accessed; whether the configuration file and the structure file of the model in the model information can be normally visualized can be checked so as to correctly check and edit the configuration file and the structure file; whether the training performance index of the pre-training model is normal or not can also be checked, in one example, when the accuracy rate in the performance index is determined to be greater than 100%, the training performance index of the model can be determined to be abnormal, and the check is not passed.
In the embodiment of the disclosure, the model information is verified through the preset standard, so that the issued pre-trained model meets the preset standard, the adaptation cost caused by inconsistency of the model information is reduced, and the availability and convenience of the issued pre-trained model are improved. The user can determine the performance effect of the model by looking at the model information to decide whether to use the model; and the warehouse address stored by the model training code can be normally accessed, so as to ensure that a user using the model can normally call the model for later model tuning.
In a possible implementation manner, the receiving a pre-training model uploaded by a user includes: receiving a pre-training model uploaded to a background management platform by a user; the issuing the pre-training model comprises: and issuing the pre-training model to an open platform.
In the embodiment of the disclosure, the pre-training model can be divided into two pieces of data, when the pre-training model is in an unpublished state, the pre-training model is stored in the background management platform, and the pre-training model in the background management platform can be visible to an uploading user and can also be visible to an administrator user in the background management platform; and when the pre-training model is in a released state, the pre-training model is copied to the public platform for storage so as to improve the safety of data, and the pre-training model in the public platform can be downloaded, tested and optimized by a user.
In one possible implementation, the method further includes: receiving an update model which is uploaded by a user and used for updating the published pre-training model, and displaying a model information input interface to the user; receiving model information of the updated model input by a user; verifying the model information of the updated model according to a preset standard; and issuing the updated model under the condition that the model information of the updated model conforms to the standard.
In the implementation manner, the user can be supported to update the issued pre-training model, the updating process of the pre-training model is similar to the process of uploading a new pre-training model, and a model information input interface can also be displayed to the user, wherein the input interface is used for the user to input the model information of the updated model, the model information of the updated model can refer to the related description of the model information, and the description is not repeated herein; after submitting the model information, the user can receive the model information of the updated model input by the user, and then check the model information of the updated model according to a preset standard, wherein the checking process can refer to the related description of the model information checking, and the description is not repeated herein; and under the condition that the model information of the updated model meets the standard, releasing the updated model.
When the update model is released, the update model can be released to the public platform, and a user can download, test and tune the update model.
In the embodiment of the disclosure, the update model can also be divided into two pieces of data, and when the update model is in an unreleased state, the update model is stored in the background management platform and can be seen by an uploading user or an administrator user in the background management platform; the pre-training model in the public platform is a pre-training model which is disclosed before, when the updating model is in a released state, the updating model is copied to the public platform for storage so as to improve the safety of data, and the updating model in the public platform can be downloaded, tested and optimized by a user.
In one possible implementation, the method further includes: comparing the updated model with the pre-training model to determine the updated information between the updated model and the pre-training model; and generating a version updating description of the pre-training model according to the updating information.
After the version of the published pre-trained model is updated, update information (publishing notice) of the version update can be displayed in each area of the interface, namely the information of the version updated relative to the previous version, and meanwhile, the model information of the version can also be displayed.
In the embodiment of the present disclosure, the updated information between the updated model and the pre-trained model, that is, the difference part between the updated model and the pre-trained model, may be determined by comparing the updated model and the pre-trained model. After the update information is determined, the update information can be displayed as a version update description of the pre-trained model.
In the embodiment of the present disclosure, the update information between the update model and the pre-training model is determined by comparing the update model and the pre-training model; and generating a version updating description of the pre-training model according to the updating information. Therefore, the update description of the update model can be automatically generated and displayed, and the generation efficiency of the version notification is improved.
In one possible implementation, the states of the pre-trained model include at least one of: an unpublished state, a published state, an updated state, and an offline state; operations supported by the pre-training model for the unpublished state include: publishing, editing, checking details and deleting; operations supported by the pre-trained model of the published state include: editing, checking details and canceling release; operations supported by the pre-trained model of the updated state include: publishing, editing, checking details and canceling publishing; operations supported by the pre-trained model of the offline state include: and (5) deleting.
Referring to fig. 3, a schematic diagram of a relationship between states of a pre-training model provided by the present disclosure is shown, in an embodiment of the present disclosure, after a user uploads the pre-training model, the model is in an unpublished state, and at this time, the user may perform publishing, editing, detail viewing, and deleting operations on the model; after the model is released, the model is in a released state, and a user can edit the model, check details and cancel releasing operation; after the user cancels the release of the released model, the model enters the offline state, and the user can delete the model aiming at the offline state; after the user updates and releases the released model, the updated model enters the updated state, and the model in the updated state can support the user to execute release, edit, view details and cancel the release.
It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted. Those skilled in the art will appreciate that in the above methods of the specific embodiments, the specific order of execution of the steps should be determined by their function and possibly their inherent logic.
In addition, the present disclosure also provides a pre-training model issuing apparatus, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any one of the pre-training model issuing methods provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the methods section are referred to and are not described again.
Fig. 4 shows a block diagram of a pre-training model issuing apparatus according to an embodiment of the present disclosure, and as shown in fig. 4, the apparatus 20 includes:
the first uploading module 21 is used for receiving the pre-training model uploaded by the user and displaying a model information input interface to the user;
a first information receiving module 22, configured to receive model information of the pre-training model input by a user;
the first checking module 23 is configured to check the model information according to a preset standard;
and the first issuing module 24 is configured to issue the pre-training model when the model information meets the standard.
In a possible implementation manner, the first uploading module is configured to receive a pre-training model uploaded to a background management platform by a user; the first publishing module is used for publishing the pre-training model to a public platform.
In one possible implementation, the model information includes at least one of:
the method comprises the following steps of model name, training task category of the model, warehouse address stored by model training codes, business item to which the model belongs, size of storage space occupied by the model, configuration file of the model, structure file of the model and training performance index of the model.
In one possible implementation, the preset criterion includes at least one of:
the model information comprises all necessary information;
a repository address of a model training code store in the model information can be accessed;
the configuration file in the model information and the structure file of the model can be normally visualized;
the training performance index of the pre-training model is normal.
In one possible implementation, the apparatus further includes:
the second uploading module is used for receiving an updating model uploaded by a user and used for updating the published pre-training model and displaying a model information input interface to the user;
the second information receiving module is used for receiving the model information of the updated model input by the user;
the second checking module is used for checking the model information of the updated model according to a preset standard;
and the second issuing module is used for issuing the updated model under the condition that the model information of the updated model meets the standard.
In one possible implementation, the apparatus further includes:
the comparison module is used for comparing the updated model with the pre-training model and determining the updated information between the updated model and the pre-training model;
and the updating description generation module is used for generating the version updating description of the pre-training model according to the updating information.
In one possible implementation, the states of the pre-trained model include at least one of:
an unpublished state, a published state, an updated state, and an offline state;
operations supported by the pre-training model for the unpublished state include: publishing, editing, checking details and deleting;
operations supported by the pre-trained model of the published state include: editing, checking details and canceling release;
operations supported by the pre-trained model of the updated state include: publishing, editing, checking details and canceling publishing;
operations supported by the pre-trained model of the offline state include: and (5) deleting.
In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a volatile or non-volatile computer readable storage medium.
An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
The disclosed embodiments also provide a computer program product comprising computer readable code or a non-transitory computer readable storage medium carrying computer readable code, which when run in a processor of an electronic device, the processor in the electronic device performs the above method.
The electronic device may be provided as a terminal, server, or other form of device.
Fig. 5 illustrates a block diagram of an electronic device 800 in accordance with an embodiment of the disclosure. For example, the electronic device 800 may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, or other terminal device.
Referring to fig. 5, electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.
The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.
The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a Complementary Metal Oxide Semiconductor (CMOS) or Charge Coupled Device (CCD) image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as a wireless network (Wi-Fi), a second generation mobile communication technology (2G), a third generation mobile communication technology (3G), a fourth generation mobile communication technology (4G), a long term evolution of universal mobile communication technology (LTE), a fifth generation mobile communication technology (5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.
The disclosure relates to the field of augmented reality, and aims to detect or identify relevant features, states and attributes of a target object by means of various visual correlation algorithms by acquiring image information of the target object in a real environment, so as to obtain an AR effect combining virtual and reality matched with specific applications. For example, the target object may relate to a face, a limb, a gesture, an action, etc. associated with a human body, or a marker, a marker associated with an object, or a sand table, a display area, a display item, etc. associated with a venue or a place. The vision-related algorithms may involve visual localization, SLAM, three-dimensional reconstruction, image registration, background segmentation, key point extraction and tracking of objects, pose or depth detection of objects, and the like. The specific application can not only relate to interactive scenes such as navigation, explanation, reconstruction, virtual effect superposition display and the like related to real scenes or articles, but also relate to special effect treatment related to people, such as interactive scenes such as makeup beautification, limb beautification, special effect display, virtual model display and the like. The detection or identification processing of the relevant characteristics, states and attributes of the target object can be realized through the convolutional neural network. The convolutional neural network is a network model obtained by performing model training based on a deep learning framework.
Fig. 6 illustrates a block diagram of an electronic device 1900 in accordance with an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server. Referring to fig. 6, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system, such as the Microsoft Server operating system (Windows Server), stored in the memory 1932TM) Apple Inc. of the present application based on the graphic user interface operating System (Mac OS X)TM) Multi-user, multi-process computer operating system (Unix)TM) Free and open native code Unix-like operating System (Linux)TM) Open native code Unix-like operating System (FreeBSD)TM) Or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A pre-training model issuing method is characterized by comprising the following steps:
receiving a pre-training model uploaded by a user, and displaying a model information input interface to the user;
receiving model information of the pre-training model input by a user;
checking the model information according to a preset standard;
and issuing the pre-training model under the condition that the model information meets the standard.
2. The method of claim 1, wherein receiving the pre-trained model uploaded by the user comprises: receiving a pre-training model uploaded to a background management platform by a user;
the issuing the pre-training model comprises: and issuing the pre-training model to an open platform.
3. The method of any of claims 1-2, wherein the model information includes at least one of:
the method comprises the following steps of model name, training task category of the model, warehouse address stored by model training codes, business item to which the model belongs, size of storage space occupied by the model, configuration file of the model, structure file of the model and training performance index of the model.
4. A method according to any of claims 1-3, wherein the predetermined criteria comprises at least one of:
the model information comprises all necessary information;
a repository address of a model training code store in the model information can be accessed;
the configuration file in the model information and the structure file of the model can be normally visualized;
the training performance index of the pre-training model is normal.
5. The method according to any one of claims 1-4, further comprising:
receiving an update model which is uploaded by a user and used for updating the published pre-training model, and displaying a model information input interface to the user;
receiving model information of the updated model input by a user;
verifying the model information of the updated model according to a preset standard;
and issuing the updated model under the condition that the model information of the updated model conforms to the standard.
6. The method of claim 5, further comprising:
comparing the updated model with the pre-training model to determine the updated information between the updated model and the pre-training model;
and generating a version updating description of the pre-training model according to the updating information.
7. The method of any of claims 1-6, wherein the states of the pre-trained model comprise at least one of:
an unpublished state, a published state, an updated state, and an offline state;
operations supported by the pre-training model for the unpublished state include: publishing, editing, checking details and deleting;
operations supported by the pre-trained model of the published state include: editing, checking details and canceling release;
operations supported by the pre-trained model of the updated state include: publishing, editing, checking details and canceling publishing;
operations supported by the pre-trained model of the offline state include: and (5) deleting.
8. A pre-trained model publishing apparatus, comprising:
the first uploading module is used for receiving the pre-training model uploaded by the user and displaying a model information input interface to the user;
the first information receiving module is used for receiving model information of the pre-training model input by a user;
the first checking module is used for checking the model information according to a preset standard;
and the first issuing module is used for issuing the pre-training model under the condition that the model information meets the standard.
9. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the memory-stored instructions to perform the method of any of claims 1 to 7.
10. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 7.
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| CN202111440634.5A CN114153540A (en) | 2021-11-30 | 2021-11-30 | Pre-training model issuing method and device, electronic equipment and storage medium |
| PCT/CN2022/088585 WO2023097952A1 (en) | 2021-11-30 | 2022-04-22 | Pre-trained model publishing method and apparatus, electronic device, storage medium, and computer program product |
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