CN111176925A - Equipment performance testing method and device and electronic equipment - Google Patents

Equipment performance testing method and device and electronic equipment Download PDF

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CN111176925A
CN111176925A CN201911347280.2A CN201911347280A CN111176925A CN 111176925 A CN111176925 A CN 111176925A CN 201911347280 A CN201911347280 A CN 201911347280A CN 111176925 A CN111176925 A CN 111176925A
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equipment
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CN111176925B (en
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周向菁
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Beijing Antutu Technology Co ltd
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Beijing Antutu Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/22—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
    • G06F11/26—Functional testing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06T5/70—

Abstract

The application provides a device performance testing method and device and electronic equipment, and belongs to the technical field of computer application. Wherein, the method comprises the following steps: when a device performance test request is acquired, acquiring a target super-resolution to generate a confrontation network model and a to-be-processed picture set, wherein the picture set comprises a plurality of pictures; generating a confrontation network model by utilizing the target super-resolution, processing each picture in the picture set to be processed, and acquiring a sharpening picture corresponding to each picture; and determining the performance of the equipment according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture. Therefore, the equipment performance testing method realizes the measurement of the AI performance of the equipment through the digital indexes and is beneficial to a user to intuitively know the AI performance of the equipment.

Description

Equipment performance testing method and device and electronic equipment
Technical Field
The present disclosure relates to the field of computer application technologies, and in particular, to a method and an apparatus for testing device performance, and an electronic device.
Background
Artificial Intelligence (AI) is a new technical science of studying and developing theories, methods, techniques and applications for simulating, extending and expanding human Intelligence. AI is used in a wide range of applications, machine translation, intelligent control, expert systems, robotics, language and image understanding, genetic programming of robotic plants, automated programming, aerospace applications, vast information processing, storage and management, performing tasks that chemical entities cannot perform, or are complex or large-scale, and the like.
In the related art, the application of the AI technology to the electronic devices is rapidly developing, and the AI performance of the electronic devices on the market is rapidly improving. However, the AI performance of electronic devices manufactured by different manufacturers and products of different generations manufactured by the same manufacturer have large differences and no digital index for measuring the AI performance of the devices exists, so that the user cannot intuitively know the AI performance of the devices.
Disclosure of Invention
The method, the device and the electronic equipment for testing the equipment performance are used for solving the problems that in the related technology, the difference of the AI performance of electronic equipment produced by different manufacturers and different generation products produced by the same manufacturer is large, and a user cannot visually know the AI performance of the equipment due to the absence of a digital index for measuring the AI performance of the equipment.
An embodiment of an aspect of the present application provides an apparatus performance testing method, including: when a device performance test request is acquired, acquiring a target super-resolution to generate a confrontation network model and a to-be-processed picture set, wherein the picture set comprises a plurality of pictures; generating a confrontation network model by utilizing the target super-resolution, processing each picture in the to-be-processed picture set, and acquiring a sharpening picture corresponding to each picture; and determining the performance of the equipment according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture.
Optionally, in a possible implementation form of the embodiment of the first aspect, the acquiring a target super-resolution generation countermeasure network model and a to-be-processed picture set when the device performance test request is acquired includes:
when an equipment performance test request is acquired, determining the type of equipment where the equipment is located currently;
and acquiring target super-resolution corresponding to the type of the equipment according to the type of the equipment to generate a confrontation network model and a picture set to be processed.
Optionally, in another possible implementation form of the embodiment of the first aspect, after determining the type of the current device, the method further includes:
determining equipment resources required for operating the super-resolution generation countermeasure network model according to the type of the equipment;
initializing the equipment resource interface;
the generation of the confrontation network model by utilizing the target super-resolution to process each picture in the picture set to be processed comprises the following steps:
and calling equipment resources through the equipment resource interface to run the target super-resolution to generate a confrontation network model, and processing each picture in the picture set to be processed.
Optionally, in yet another possible implementation form of the embodiment of the first aspect, the apparatus includes a display screen;
the method further comprises the following steps:
and displaying the target super-resolution in the equipment display screen to generate a target display picture corresponding to a first picture currently processed by a confrontation network model, wherein the definition of the target display picture is different from that of the first picture.
Optionally, in yet another possible implementation form of the embodiment of the first aspect, before displaying, in the device display screen, a target display picture corresponding to the first picture currently processed by the target super-resolution generation countermeasure network model, the method further includes:
determining the definition of a target display picture corresponding to each picture in the picture set according to the number of the pictures in the picture set to be processed and the processing sequence of each picture, wherein the definition of the target display picture corresponding to different pictures is different;
and according to the definition of a target display picture corresponding to the first picture, carrying out definition processing on the first picture to generate the target display picture.
Optionally, in another possible implementation form of the embodiment of the first aspect, after determining the performance of the device, the method further includes:
displaying, in the display screen, a performance of the device.
Optionally, in another possible implementation form of the embodiment of the first aspect, before the obtaining the target super-resolution according to the type of the device to generate the confrontation network model, the method further includes:
training an initial super-resolution generation confrontation network model based on preset open source software and a preset open source data set;
and converting the initial super-resolution generation confrontation network model into each target super-resolution generation confrontation network model corresponding to each type of equipment by using a model conversion tool of each equipment provider.
Optionally, in yet another possible implementation form of the embodiment of the first aspect, before the determining the performance of the device, the method further includes:
determining the target super-resolution generation confrontation network model, and processing time of each picture when each picture is subjected to sharpening processing;
the determining the performance of the device comprises:
determining a signal-to-noise ratio difference value accumulated value according to the difference value between the peak signal-to-noise ratio of the corresponding clarified picture and the peak signal-to-noise ratio of each picture in the picture set;
and determining the frame number of the pictures of the equipment transmitted per second according to the processing time of each picture in the picture set.
The device performance testing apparatus provided in another embodiment of the present application includes: the device comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a target super-resolution to generate a confrontation network model and a to-be-processed picture set when acquiring a device performance test request, and the picture set comprises a plurality of pictures; the processing module is used for generating a confrontation network model by utilizing the target super-resolution, processing each picture in the to-be-processed picture set and acquiring a sharpening picture corresponding to each picture; and the second determining module is used for determining the performance of the equipment according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture.
Optionally, in a possible implementation form of the embodiment of the second aspect, the obtaining module is specifically configured to:
when an equipment performance test request is acquired, determining the type of equipment where the equipment is located currently;
and acquiring target super-resolution corresponding to the type of the equipment according to the type of the equipment to generate a confrontation network model and a picture set to be processed.
Optionally, in another possible implementation form of the embodiment of the second aspect, the apparatus further includes:
the second determination module is used for determining equipment resources required for operating the super-resolution generation countermeasure network model according to the type of the equipment;
the initialization module is used for initializing the equipment resource interface;
the processing module is specifically configured to:
and calling equipment resources through the equipment resource interface to run the target super-resolution to generate a confrontation network model, and processing each picture in the picture set to be processed.
Optionally, in yet another possible implementation form of the embodiment of the second aspect, the device includes a display screen; the device, still include:
and the first display module is used for displaying a target display picture corresponding to a first picture currently processed by the confrontation network model by the target super-resolution generation in the equipment display screen, wherein the definition of the target display picture is different from that of the first picture.
Optionally, in another possible implementation form of the embodiment of the second aspect, the apparatus further includes:
the third determining module is used for determining the definition of a target display picture corresponding to each picture in the picture set according to the number of the pictures in the picture set to be processed and the processing sequence of each picture, wherein the definition of the target display picture corresponding to different pictures is different;
and the generation module is used for carrying out sharpening processing on the first picture according to the definition of the target display picture corresponding to the first picture so as to generate the target display picture.
Optionally, in another possible implementation form of the embodiment of the second aspect, the apparatus further includes:
and the second display module is used for displaying the performance of the equipment in the display screen.
Optionally, in another possible implementation form of the embodiment of the second aspect, the apparatus further includes:
the training module is used for training the initial super-resolution to generate a confrontation network model based on preset open source software and a preset open source data set;
and the conversion module is used for converting the initial super-resolution generation confrontation network model into each target super-resolution generation confrontation network model corresponding to each type of equipment by using a model conversion tool of each equipment manufacturer.
Optionally, in another possible implementation form of the embodiment of the second aspect, the apparatus further includes:
the fourth determining module is used for determining the target super-resolution generation confrontation network model and processing time of each picture when each picture is subjected to sharpening processing;
the first determining module is specifically configured to:
determining a signal-to-noise ratio difference value accumulated value according to the difference value between the peak signal-to-noise ratio of the corresponding clarified picture and the peak signal-to-noise ratio of each picture in the picture set;
and determining the frame number of the pictures of the equipment transmitted per second according to the processing time of each picture in the picture set.
An embodiment of another aspect of the present application provides an electronic device, which includes: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the device performance testing method as described above when executing the program.
In another aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the device performance testing method as described above.
In another aspect of the present application, a computer program is provided, which is executed by a processor to implement the device performance testing method according to the embodiment of the present application.
According to the device performance testing method, the device, the electronic device, the computer-readable storage medium and the computer program, when the device performance testing request is obtained, the target super-resolution is obtained to generate the confrontation network model and the picture set which is to be processed and comprises a plurality of pictures, the confrontation network model is generated by utilizing the target super-resolution, each picture in the picture set to be processed is processed to obtain the sharpening picture corresponding to each picture, and then the performance of the device is determined according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture. Therefore, the image set in the equipment is subjected to sharpening processing by utilizing the target super-resolution generation countermeasure network model, the AI performance of the equipment is determined according to the sharpness of the image set subjected to sharpening processing by utilizing the target super-resolution generation countermeasure network model, the AI performance of the equipment is measured through the digital indexes, and the user can visually know the AI performance of the equipment.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of a device performance testing method according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of another device performance testing method according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of another method for testing device performance according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of the performance of a display device in a display screen of the device;
FIG. 5 is a schematic diagram of another capability of a display device in a display screen of the device;
fig. 6 is a schematic structural diagram of an apparatus performance testing device according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the like or similar elements throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The embodiment of the application provides a method for testing the performance of equipment, aiming at the problems that in the related art, the difference of the AI performance of electronic equipment produced by different manufacturers and different generation products produced by the same manufacturer is large, and the digital index for measuring the AI performance of the equipment does not exist, so that a user cannot intuitively know the AI performance of the equipment.
According to the device performance testing method provided by the embodiment of the application, when the device performance testing request is obtained, the target super-resolution is obtained to generate the confrontation network model and the picture set to be processed and comprising the multiple pictures, the confrontation network model is generated by utilizing the target super-resolution, each picture in the picture set to be processed is processed to obtain the sharpening picture corresponding to each picture, and then the performance of the device is determined according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture. Therefore, the image set in the equipment is subjected to sharpening processing by utilizing the target super-resolution generation countermeasure network model, the AI performance of the equipment is determined according to the sharpness of the image set subjected to sharpening processing by utilizing the target super-resolution generation countermeasure network model, the AI performance of the equipment is measured through the digital indexes, and the user can visually know the AI performance of the equipment.
The device performance testing method, apparatus, electronic device, storage medium, and computer program provided by the present application are described in detail below with reference to the accompanying drawings.
The device performance testing method provided by the embodiment of the present application is described in detail below with reference to fig. 1.
Fig. 1 is a schematic flow chart of a device performance testing method according to an embodiment of the present disclosure.
As shown in fig. 1, the device performance testing method includes the following steps:
step 101, when a device performance test request is acquired, acquiring a target super-resolution to generate a confrontation network model and a to-be-processed picture set, wherein the picture set comprises a plurality of pictures.
It should be noted that the device performance testing method according to the embodiment of the present application may be executed by the device performance testing apparatus according to the embodiment of the present application. The device performance testing apparatus of the embodiment of the present application may be configured in any electronic device, such as a mobile phone, a tablet computer, a personal digital assistant, a wearable device, and the like, which is not limited in the embodiment of the present application. In practical use, the device performance testing method according to the embodiment of the present application may be applied to any scenario of testing the performance of a device, and the following description will take an AI performance applied to a testing device as an example to describe in detail.
The device performance test request may be actively input by a user through an input device (such as a mouse, a keyboard, a touch screen, etc.) of the device, or may be automatically generated after the device is turned on for the first time or each time. For example, an option of "device performance test" may be provided in a "setup" menu of the device, so that the device may generate a device performance test request when detecting that the option is clicked; or, a code for triggering the device performance test may be set in the boot program of the device, so that the device performance test may be actively triggered when the device is turned on and off for the first time or is booted each time, and a device performance test request may be generated.
The confrontation network model generated by the target super-resolution is a model which is suitable for pre-training and can carry out sharpening processing on pictures.
The picture set to be processed may be a gallery (such as an album) in the device itself, or may be a test picture set preset for testing the performance of the device, which is not limited in the embodiment of the present application.
It should be noted that, corresponding Peak Signal to Noise Ratio (PSNR) is labeled for each of the multiple pictures included in the picture set to be processed, that is, the PSNR of each picture in the picture set to be processed may be calculated, and the calculated PSNR of each picture is used to label the corresponding picture. Therefore, when the performance of the equipment is tested, the target super-resolution can be judged according to the PSNR of the pictures in the picture set to be processed, and the optimization degree of the confrontation network model on the picture definition can be generated.
In the embodiment of the application, when the device performance test request is acquired, the super-resolution of the target in the current device can be determined to generate the confrontation network model and the to-be-processed picture set for the performance test.
Furthermore, because different types of devices may have certain differences in operating environments, software and hardware configurations, and the like, different super-resolution generation countermeasure network models can be used for performing performance tests on the different types of devices, so that the super-resolution generation countermeasure network models can operate in the corresponding devices. That is, in a possible implementation form of the embodiment of the present application, the step 101 may include:
when an equipment performance test request is acquired, determining the type of equipment where the equipment is located currently;
and acquiring target super-resolution corresponding to the type of the equipment according to the type of the equipment to generate a confrontation network model and a picture set to be processed.
The type of the device may be information of a manufacturer of the device, a model of the device, and the like. In actual use, the device information corresponding to the type of the device can be preset according to actual needs. For example, the type of device may be preset to the model of the device.
In the embodiment of the application, when the processor of the current device obtains the device performance test request, the type of the device may be obtained from the memory of the current device, and according to the type of the current device, the target classification model corresponding to the current device and the to-be-processed picture set are determined. The type of the device may be preset in the memory at the time of factory shipment of the device.
Furthermore, a general initial super-resolution generation countermeasure network model can be trained by using the open source software and the open source data set, and the initial super-resolution generation countermeasure network model is converted by using a model conversion tool corresponding to each type of device, so as to obtain a target super-resolution generation countermeasure network model corresponding to each type of device. That is, in a possible implementation form of the embodiment of the present application, before the step 101, the method may further include:
training an initial super-resolution generation confrontation network model based on preset open source software and a preset open source data set;
and respectively converting the initial super-resolution into the confrontation network model by using the model conversion tool of each equipment manufacturer, and respectively converting the initial super-resolution into the target super-resolution corresponding to each type of equipment to generate the confrontation network model.
As a possible implementation manner, corresponding open source software and an open source data set can be acquired from a network as a preset open source software and an open source data set according to actual equipment performance test requirements, and then the preset open source software and the preset open source data set are utilized to train the initial super-resolution to generate the confrontation network model. And then, converting the obtained initial super-resolution generation countermeasure network model by using a model conversion tool provided by each equipment provider to obtain each target super-resolution generation countermeasure network model corresponding to each type of equipment, so that each target super-resolution generation countermeasure network model is adapted to the running environment, software and hardware configuration and the like of the corresponding type of equipment.
For example, in the current application scenario, if the AI performance of the device is tested, the artificial intelligence library tensoflow of google may be used as the preset open source software, the data set ImageNet is used as the preset open source data set, and the deep convolutional neural network model SRGAN is trained to obtain the initial super-resolution to generate the countermeasure network model. After the initial super-resolution generation countermeasure network model is trained, the initial super-resolution generation countermeasure network model may be converted according to a model conversion tool of an AI Software Development Kit (SDK) provided by each device vendor, so as to obtain a target super-resolution generation countermeasure network model corresponding to each type of device.
And 102, generating a confrontation network model by utilizing the target super-resolution, processing each picture in the picture set to be processed, and acquiring a sharpening picture corresponding to each picture.
The sharpening picture corresponding to the picture is the picture generated after the target super-resolution generation confrontation network model sharpens the picture.
In the embodiment of the application, after the target super-resolution generation confrontation network model corresponding to the current device is determined, the target super-resolution generation confrontation network model can be used for carrying out sharpening processing on each picture in a picture set to be processed, so that the sharpness of each picture is improved, and a sharpened picture corresponding to each picture is generated.
And 103, determining the performance of the equipment according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the corresponding sharpening picture of each picture.
The Peak Signal to Noise Ratio (PSNR) is an index for evaluating the picture quality. Specifically, the larger the PSNR of a picture is, the better the picture quality is; the smaller the PSNR of a picture, the worse the picture quality.
In the embodiment of the application, the difference value between the PSNR of each picture in the to-be-processed picture set and the PSNR of the sharpening picture corresponding to each picture can be used for measuring the optimization effect of the target super-resolution generation countermeasure network model on the sharpness of each picture in the to-be-processed picture set. Specifically, the larger the difference between the PSNR of the sharpening picture corresponding to the picture and the PSNR of the picture is, the better the effect of the target super-resolution generation countermeasure network model on the sharpness optimization of the picture can be determined; on the contrary, the poorer the definition optimization effect of the target super-resolution generation countermeasure network model on the picture can be determined. Therefore, the optimization effect of the equipment on the definition of the picture can be determined according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the definition picture corresponding to each picture.
As a possible implementation manner, after each picture in the to-be-processed picture set is processed, an average value of differences between the PSNR of the sharpening picture corresponding to each picture in the to-be-processed picture set and the PSNR of each picture (that is, an average sharpness optimization effect of the to-be-processed picture set) may be determined, and then the average sharpness optimization effect is determined as the performance of the device. That is, the larger the average value of the difference between the PSNR of the sharpening picture corresponding to each picture and the PSNR of each picture (i.e., the average sharpness optimization effect), the better the performance of the device; the smaller the average value of the difference between the PSNR of the sharpening picture corresponding to each picture and the PSNR of each picture (i.e., the average sharpness optimization effect), the worse the performance of the device.
Optionally, in this embodiment of the application, an accumulated value of the number of pictures processed by the device per second and a difference between a peak signal-to-noise ratio of the sharpening picture corresponding to each picture and a peak signal-to-noise ratio of each picture may also be used as an index for measuring the performance of the device. That is, in a possible implementation form of the embodiment of the present application, before the step 103, the method may further include:
determining the target super-resolution generation confrontation network model, and processing time of each picture when each picture is subjected to sharpening processing;
accordingly, the step 103 may include:
determining a signal-to-noise ratio difference value accumulated value according to the difference value of the peak signal-to-noise ratio of the corresponding clarified picture and the peak signal-to-noise ratio of each picture in the picture set;
and determining the frame number of the pictures of the equipment transmitted per second according to the processing time of each picture in the picture set.
In the embodiment of the application, the processing time of each picture can reflect the processing speed of the equipment for sharpening the picture, and the difference value between the peak signal-to-noise ratio of the sharpening picture corresponding to each picture and the peak signal-to-noise ratio of each picture can reflect the reliability of sharpening of the picture by the equipment, so that when the countermeasure network model is generated by utilizing the target super-resolution to sharpen each picture in the set of pictures to be processed, the processing time of the target classification model for each picture can be recorded for measuring the processing speed of sharpening of the picture by the equipment.
The processing time refers to the time required for the target super-resolution generation countermeasure network model to start processing a picture until a sharpening picture corresponding to the picture is generated. That is, the processing time may reflect the processing speed of the device currently located.
The Frame Per Second (FPS) refers to the number of pictures in a moving picture or a video. In the embodiment of the present application, the number of pictures processed per second by the target super-resolution generation confrontation network model corresponding to the current device may be calculated by formula (1):
Figure BDA0002333735090000091
wherein n is the number of the aggregated pictures of the pictures to be processed, TiThe processing time of the ith picture in the picture set to be processed is shown, and i is the serial number of the picture in the picture set to be processed.
As can be seen from formula (1), FPS in the embodiment of the present application is an average value of the reciprocal of the processing time of each picture in the to-be-processed picture set.
In the embodiment of the application, the optimization effect of the equipment on the definition of the picture can be measured through the accumulated value of the difference value of the definition picture corresponding to each picture and the peak signal to noise ratio of each picture. The difference value between the PSNR of the sharpening picture corresponding to each picture and the PSNR of each picture is determined according to the PSNR of each picture in the picture set to be processed and the PSNR of the sharpening picture corresponding to each picture, and then all the determined PSNR difference values are accumulated, so that the signal-to-noise ratio difference value accumulated value is determined. The accumulated snr difference value can be calculated by equation (2):
Figure BDA0002333735090000092
wherein, Δ P is the accumulated value of the SNR difference, n is the number of the lumped pictures of the pictures to be processed, aiPSNR, b for the ith picture in a set of pictures to be processediThe PSNR is the PSNR of the sharpening picture corresponding to the ith picture in the picture set to be processed, and i is the serial number of the picture in the picture set to be processed.
In the embodiment of the application, when an FPS of equipment and an accumulated value of a signal-to-noise ratio difference value of the equipment for performing the sharpening processing on a picture set to be processed are used as two indexes for measuring the performance of the equipment, the larger the FPS is, the larger the accumulated value of the signal-to-noise ratio difference value is, the better the performance of the equipment is; the smaller the FPS and the smaller the accumulated value of the signal-to-noise ratio difference value, the worse the performance of the equipment.
According to the device performance testing method provided by the embodiment of the application, when the device performance testing request is obtained, the target super-resolution is obtained to generate the confrontation network model and the picture set to be processed and comprising the multiple pictures, the confrontation network model is generated by utilizing the target super-resolution, each picture in the picture set to be processed is processed to obtain the sharpening picture corresponding to each picture, and then the performance of the device is determined according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture. Therefore, the image set in the equipment is subjected to sharpening processing by utilizing the target super-resolution generation countermeasure network model, the AI performance of the equipment is determined according to the sharpness of the image set subjected to sharpening processing by utilizing the target super-resolution generation countermeasure network model, the AI performance of the equipment is measured through the digital indexes, and the user can visually know the AI performance of the equipment.
In a possible implementation form of the present application, since the operating environments, software and hardware configurations, and the like of different types of devices may be different, resource interfaces for operating the target super-resolution generation countermeasure network model may also be different, and thus the device resource interfaces may be determined and initialized according to the types of the devices.
The device performance testing method provided by the embodiment of the present application is further described below with reference to fig. 2.
Fig. 2 is a schematic flow chart of another device performance testing method according to an embodiment of the present disclosure.
As shown in fig. 2, the device performance testing method includes the following steps:
step 201, when the device performance test request is obtained, determining the type of the current device.
The detailed implementation process and principle of step 201 may refer to the detailed description of the above embodiments, and are not described herein again.
Step 202, determining the device resources required for operating the target super-resolution generation countermeasure network model according to the type of the device.
Step 203, initialize the device resource interface.
In the embodiment of the application, because different types of devices may have certain differences in operating environments, software and hardware configurations, and the like, after the type of the current device is determined, whether the current device supports the target super-resolution generation countermeasure network model or not can be determined according to the type of the device, if so, device resources required by the target super-resolution generation countermeasure network model are further determined, and a device resource interface is initialized.
For example, if the current application scenario is to test the AI performance of the device, the AI function support condition of the current device may be determined according to the type of the current device, and when the current device supports the AI function, the AI processing interface (i.e., the device resource interface required by the target super-resolution generation countermeasure network model) in the current device is determined, and the AI processing interface is initialized, so that the target super-resolution generation countermeasure network model may obtain the required device resource through the AI processing interface.
And 204, acquiring the target super-resolution according to the type of the equipment to generate a confrontation network model and a picture set to be processed, wherein the picture set comprises a plurality of pictures.
The detailed implementation process and principle of the step 204 may refer to the detailed description of the above embodiments, and are not described herein again.
Step 205, calling the device resource to run the target super-resolution through the device resource interface to generate a confrontation network model, processing each picture in the to-be-processed picture set, and acquiring a sharpening picture corresponding to each picture.
In the embodiment of the application, when the countermeasure network model is generated by using the target super-resolution to perform sharpening processing on each picture, the device resource can be called through the determined device resource interface to run the target super-resolution to generate the countermeasure network model.
In the step 205, a concrete implementation process and principle of sharpening each picture by using the target super-resolution generation confrontation network model and obtaining a sharpened picture corresponding to each picture can refer to the detailed description of the above embodiment, and details are not repeated here.
And step 206, determining the performance of the equipment according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture.
The detailed implementation process and principle of the step 206 may refer to the detailed description of the above embodiments, and are not described herein again.
According to the device performance testing method provided by the embodiment of the application, when a device performance testing request is obtained, the type of the current device is determined, the device resource required by running the target super-resolution to generate the confrontation network model and the device resource interface are determined according to the device type, then the target super-resolution is determined according to the device type to generate the confrontation network model and the picture set to be processed, the device resource is called to run the target super-resolution to generate the confrontation network model through the device resource interface, each picture is subjected to sharpening processing, a sharpened picture corresponding to each picture is determined, and the performance of the device is determined according to the PSNR of each picture and the PSNR of the sharpened picture corresponding to each picture. Therefore, the picture set in the equipment is subjected to sharpening processing by utilizing the target super-resolution generation countermeasure network model matched with the equipment, the sharpness of the sharpening processing of the picture by the countermeasure network model generated according to the target super-resolution is determined, the AI performance of the equipment is determined, and the equipment resource interface required by operating the target super-resolution generation countermeasure network model is determined according to the equipment type, so that the AI performance of the equipment is measured through the digital indexes, the AI performance of the equipment is visually known by a user, and the successful operation of the target super-resolution generation countermeasure network model in the equipment is ensured.
In one possible implementation form of the application, when the performance of the equipment is tested, the process and the result of the performance test can be fed back in the output device of the equipment, so that the friendliness and the interchangeability of the performance test of the equipment are improved.
The device performance testing method provided in the embodiment of the present application is further described below with reference to fig. 3.
Fig. 3 is a schematic flowchart of another device performance testing method according to an embodiment of the present disclosure.
As shown in fig. 3, the device performance testing method includes the following steps:
step 301, when a device performance test request is obtained, obtaining a target super-resolution to generate a confrontation network model and a to-be-processed picture set, wherein the picture set comprises a plurality of pictures.
And 302, generating a confrontation network model by utilizing the target super-resolution, processing each picture in the picture set to be processed, and acquiring a sharpening picture corresponding to each picture.
The detailed implementation process and principle of the steps 301 to 302 may refer to the detailed description of the above embodiments, and are not described herein again.
And 303, displaying the target super-resolution in the display screen of the equipment to generate a target display picture corresponding to a first picture currently processed by the confrontation network model, wherein the definition of the target display picture is different from that of the first picture.
In the embodiment of the application, if the current device includes the display screen, the process of performing the performance test on the current device and the test result can be displayed on the display screen of the device. Therefore, the target super-resolution generation confronts the clarification processing process of the network model to the picture set to be processed, and the picture set to be processed is displayed in the display screen in the form of animation, so that the friendliness and the interchangeability of the test interface are improved.
The first picture is any one picture in a to-be-processed picture set currently processed by the countermeasure network model and generated by target super-resolution.
The target display picture corresponding to the first picture is a picture displayed in a display screen in the process of processing the first picture when the sharpening process of the picture set to be processed is displayed. The definition of the target display picture corresponding to the first picture may be higher than the definition of the first picture, that is, the PSNR of the display picture corresponding to the first picture may be greater than the PSNR of the first picture.
As a possible implementation, the process of generating the target super-resolution in the form of animation in the display screen of the device to sharpen each picture in the set of pictures to be processed against the network model may be displayed. The method can display the process of the sharpening of the picture set to be processed in the display screen of the equipment in an animation mode, each frame of picture in the animation is a target display picture corresponding to the first picture, and friendliness and interchangeability of a test interface are improved.
Further, when the sharpening process of the to-be-processed picture set is presented in an animation form, each frame of picture in the animation can be blurred to be sharpened, so that a target display picture corresponding to the first picture can be generated according to the processed sequence of the first picture. That is, in a possible implementation form of the embodiment of the present application, before the step 303, the method may further include:
determining the definition of a target display picture corresponding to each picture in the picture set according to the number of the pictures in the picture set to be processed and the processed sequence of each picture, wherein the definition of the target display picture corresponding to different pictures is different;
according to the definition of the target display picture corresponding to the first picture,
and carrying out sharpening processing on the first picture to generate a target display picture.
The definition of the target display picture may be represented by PSNR of the target display picture, which is not limited in this embodiment of the present application. The following description will be made in detail by taking PSNR of a target display picture as an example.
As a possible implementation manner, during a sharpening process of displaying a to-be-processed picture set from blur to sharpness in an animation form, an initial PSNR and a PSNR step length of a target display picture corresponding to each picture in the to-be-processed picture set may be preset, and then, a PSNR of the target display picture corresponding to each picture may be determined according to a processed sequence of each picture in the to-be-processed picture set, and then, during sharpening of a first picture, a target display picture corresponding to the first picture may be generated according to the PSNR of the target display picture corresponding to the first picture, so as to be displayed on a display screen of a device.
Specifically, assuming that a preset initial PSNR is a and a PSNR step length is k, if a processed sequence of a first picture is 1 (that is, the first picture is a 1 st picture in a picture set to be processed), it may be determined that a definition of a target display picture corresponding to the first picture is a, so that when performing a definition process on the first picture, the target display picture with the definition of a may be generated and displayed on a display screen of a device; if the display sequence of the first picture is N, it may be determined that the definition of the target display picture corresponding to the first picture is a + (N-1) k, so that when the first picture is subjected to the definition processing, the target display picture with the definition of a + (N-1) k may be generated and displayed on the display screen of the device.
As a possible implementation manner, the PSNR minimum and maximum of the target display picture corresponding to each picture in the to-be-processed picture set may also be preset, so that the definition of the target display picture corresponding to each picture may be determined according to the number of pictures in the to-be-processed picture set and the processed sequence of each picture.
Specifically, assuming that the preset PSNR minimum value is x and the preset PSNR maximum value is y, the number of pictures in the to-be-processed picture set is N, and the definition of the target display picture corresponding to the picture with the processing sequence N is: x + (N-1) (y-x)/N.
For example, an old person can be shown in animation in a display screen of the device to see an album with a chronological sense, and when the album is turned over, each photo in the album is clearer relative to the previous photo, so that the process of sharpening each photo in the set of to-be-processed pictures by the device is shown. Namely, the current displayed picture in the photo album turned by the old is the target display picture corresponding to the first picture currently processed by the equipment, after the equipment finishes processing the current processed first picture, the old in the animation is controlled to turn the photo album, and the target display picture corresponding to the next picture in the picture set to be processed is displayed in the photo album, so that the effect of the animation of the pictures in the photo album from fuzzy to clear is presented.
And step 304, determining the performance of the equipment according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture.
The detailed implementation process and principle of the step 304 may refer to the detailed description of the above embodiments, and are not repeated herein.
Step 305, displaying the performance of the device in the display screen.
In the embodiment of the application, if the current device includes the display screen, after the performance of the current device is determined, the performance of the device can be displayed in the display screen.
For example, the performance of the device only includes one parameter of the snr difference accumulation value, the snr difference accumulation value of the device is determined to be Y, and a schematic diagram of displaying the performance of the device is shown in fig. 4; for another example, the performance of the device includes an FPS and an accumulated snr difference value, the determined FPS of the device is X, the accumulated snr difference value is Y, and a schematic diagram showing the performance of the device is shown in fig. 5.
According to the device performance testing method provided by the embodiment of the application, when a device performance testing request is obtained, a target super-resolution is obtained to generate a confrontation network model and a to-be-processed picture set, the confrontation network model is generated by utilizing the target super-resolution, each picture is processed, a sharpening picture corresponding to each picture is obtained, then a target display picture corresponding to a first picture currently processed by the confrontation network model is generated by displaying the target super-resolution in a device display screen, and the performance of the device is determined and displayed in the display screen according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture. Therefore, the image set in the equipment is subjected to sharpening processing by utilizing the target super-resolution generation countermeasure network model, the sharpness of the sharpening processing of the image set by utilizing the target super-resolution generation countermeasure network model is obtained according to the target super-resolution generation countermeasure network model, the AI performance of the equipment is determined, and the process and the result of the performance test are displayed in the display screen of the equipment, so that the AI performance of the equipment is measured through digital indexes, the AI performance of the equipment can be intuitively known by a user, and the friendliness and the interchangeability of a test interface are improved.
In order to implement the above embodiments, the present application further provides an apparatus performance testing device.
Fig. 6 is a schematic structural diagram of an apparatus performance testing device according to an embodiment of the present application.
As shown in fig. 6, the device performance testing apparatus 40 includes:
the obtaining module 41 is configured to, when a device performance test request is obtained, obtain a target super-resolution to generate a confrontation network model and a to-be-processed picture set, where the picture set includes multiple pictures;
the processing module 42 is configured to generate a confrontation network model by using the target super-resolution, process each picture in the set of pictures to be processed, and acquire a sharpening picture corresponding to each picture;
the first determining module 43 is configured to determine the performance of the device according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture.
In practical use, the device performance testing apparatus provided in the embodiment of the present application may be configured in any electronic device to execute the device performance testing method.
According to the device performance testing device provided by the embodiment of the application, when the device performance testing request is obtained, the target super-resolution is obtained to generate the confrontation network model and the picture set to be processed and comprising the multiple pictures, the confrontation network model is generated by utilizing the target super-resolution, each picture in the picture set to be processed is processed to obtain the sharpening picture corresponding to each picture, and then the performance of the device is determined according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture. Therefore, the image set in the equipment is subjected to sharpening processing by utilizing the target super-resolution generation countermeasure network model, the AI performance of the equipment is determined according to the sharpness of the image set subjected to sharpening processing by utilizing the target super-resolution generation countermeasure network model, the AI performance of the equipment is measured through the digital indexes, and the user can visually know the AI performance of the equipment.
In a possible implementation form of the present application, the obtaining module 41 is specifically configured to:
when an equipment performance test request is acquired, determining the type of equipment where the equipment is located currently;
and acquiring target super-resolution corresponding to the type of the equipment according to the type of the equipment to generate a confrontation network model and a picture set to be processed.
In a possible implementation form of the present application, the device performance testing apparatus 40 further includes:
the second determination module is used for determining equipment resources required by running the super-resolution generation countermeasure network model according to the type of the equipment;
the initialization module is used for initializing the equipment resource interface;
correspondingly, the processing module 42 is specifically configured to:
and calling the equipment resource to run the target super-resolution through the equipment resource interface to generate a confrontation network model, and processing each picture in the picture set to be processed.
Further, in another possible implementation form of the present application, the device includes a display screen; the device performance testing apparatus 40 further includes:
and the first display module is used for displaying the target super-resolution in the equipment display screen to generate a target display picture corresponding to a first picture currently processed by the confrontation network model, wherein the definition of the target display picture is different from that of the first picture.
Further, in another possible implementation form of the present application, the device performance testing apparatus 40 further includes:
the third determining module is used for determining the definition of a target display picture corresponding to each picture in the picture set according to the number of the pictures in the picture set to be processed and the processed sequence of each picture, wherein the definition of the target display picture corresponding to different pictures is different;
and the generation module is used for carrying out sharpening processing on the first picture according to the definition of the target display picture corresponding to the first picture so as to generate the target display picture.
Further, in another possible implementation form of the present application, the device performance testing apparatus 40 further includes:
and the second display module is used for displaying the performance of the equipment in the display screen.
Further, in another possible implementation form of the present application, the device performance testing apparatus 40 further includes:
the training module is used for training the initial super-resolution to generate a confrontation network model based on preset open source software and a preset open source data set;
and the conversion module is used for converting the initial super-resolution generation confrontation network model into each target super-resolution generation confrontation network model corresponding to each type of equipment by using the model conversion tool of each equipment manufacturer.
Further, in another possible implementation form of the present application, the device performance testing apparatus 40 further includes:
the fourth determining module is used for determining the target super-resolution generation confrontation network model and processing time of each picture when each picture is subjected to sharpening processing;
the first determining module 43 is specifically configured to:
determining a signal-to-noise ratio difference value accumulated value according to the difference value of the peak signal-to-noise ratio of the corresponding clarified picture and the peak signal-to-noise ratio of each picture in the picture set;
and determining the number of frames transmitted per second of the equipment pictures according to the processing time of each picture in the picture set.
It should be noted that the foregoing explanation on the device performance testing method embodiments shown in fig. 1, fig. 2, and fig. 3 also applies to the device performance testing apparatus 40 of this embodiment, and details are not repeated here.
According to the device performance testing device provided by the embodiment of the application, when a device performance testing request is obtained, a target super-resolution is obtained to generate a confrontation network model and a to-be-processed picture set, the confrontation network model is generated by utilizing the target super-resolution, each picture is processed, a sharpening picture corresponding to each picture is obtained, then a target display picture corresponding to a first picture currently processed by the confrontation network model is generated by displaying the target super-resolution in a device display screen, and the performance of the device is determined and displayed in the display screen according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture. Therefore, the image set in the equipment is subjected to sharpening processing by utilizing the target super-resolution generation countermeasure network model, the sharpness of the sharpening processing of the image set by utilizing the target super-resolution generation countermeasure network model is obtained according to the target super-resolution generation countermeasure network model, the AI performance of the equipment is determined, and the process and the result of the performance test are displayed in the display screen of the equipment, so that the AI performance of the equipment is measured through digital indexes, the AI performance of the equipment can be intuitively known by a user, and the friendliness and the interchangeability of a test interface are improved.
In order to implement the above embodiments, the present application further provides an electronic device.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
As shown in fig. 7, the electronic device 200 includes:
a memory 210 and a processor 220, a bus 230 connecting different components (including the memory 210 and the processor 220), wherein the memory 210 stores a computer program, and when the processor 220 executes the program, the device performance testing method according to the embodiment of the present application is implemented.
Bus 230 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Electronic device 200 typically includes a variety of electronic device readable media. Such media may be any available media that is accessible by electronic device 200 and includes both volatile and nonvolatile media, removable and non-removable media.
Memory 210 may also include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)240 and/or cache memory 250. The electronic device 200 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 260 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 7, commonly referred to as a "hard drive"). Although not shown in FIG. 7, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 230 by one or more data media interfaces. Memory 210 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.
A program/utility 280 having a set (at least one) of program modules 270, including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof may comprise an implementation of a network environment, may be stored in, for example, the memory 210. The program modules 270 generally perform the functions and/or methodologies of the embodiments described herein.
Electronic device 200 may also communicate with one or more external devices 290 (e.g., keyboard, pointing device, display 291, etc.), with one or more devices that enable a user to interact with electronic device 200, and/or with any devices (e.g., network card, modem, etc.) that enable electronic device 200 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 292. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 293. As shown, the network adapter 293 communicates with the other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processor 220 executes various functional applications and data processing by executing programs stored in the memory 210.
It should be noted that, for the implementation process and the technical principle of the electronic device of this embodiment, reference is made to the foregoing explanation of the device performance testing method of the embodiment of the present application, and details are not described here again.
The electronic device provided by the embodiment of the application can execute the device performance testing method, when the device performance testing request is obtained, the target super-resolution is obtained to generate the confrontation network model and the to-be-processed picture set comprising the multiple pictures, the confrontation network model is generated by utilizing the target super-resolution, each picture in the to-be-processed picture set is processed to obtain the sharpening picture corresponding to each picture, and then the performance of the device is determined according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture. Therefore, the image set in the equipment is subjected to sharpening processing by utilizing the target super-resolution generation countermeasure network model, the AI performance of the equipment is determined according to the sharpness of the image set subjected to sharpening processing by utilizing the target super-resolution generation countermeasure network model, the AI performance of the equipment is measured through the digital indexes, and the user can visually know the AI performance of the equipment.
In order to implement the above embodiments, the present application also proposes a computer-readable storage medium.
The computer readable storage medium stores thereon a computer program, and the computer program is executed by a processor to implement the device performance testing method according to the embodiment of the present application.
In order to implement the foregoing embodiments, a further embodiment of the present application provides a computer program, which is executed by a processor to implement the device performance testing method according to the embodiments of the present application.
In an alternative implementation, the embodiments may be implemented in any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the consumer electronic device, partly on the consumer electronic device, as a stand-alone software package, partly on the consumer electronic device and partly on a remote electronic device, or entirely on the remote electronic device or server. In the case of remote electronic devices, the remote electronic devices may be connected to the consumer electronic device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external electronic device (e.g., through the internet using an internet service provider).
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (10)

1. An apparatus performance testing method, comprising:
acquiring a target super-resolution to generate a confrontation network model and a picture set to be processed, wherein the picture set comprises a plurality of pictures;
generating a confrontation network model by utilizing the target super-resolution, processing each picture in the to-be-processed picture set, and acquiring a sharpening picture corresponding to each picture;
and determining the performance of the equipment according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture.
2. The method of claim 1, wherein the obtaining target super-resolution generation countermeasure network model and the to-be-processed picture set upon obtaining the device performance test request comprises:
when an equipment performance test request is acquired, determining the type of equipment where the equipment is located currently;
and acquiring target super-resolution corresponding to the type of the equipment according to the type of the equipment to generate a confrontation network model and a picture set to be processed.
3. The method of claim 2, wherein after determining the type of the currently located device, further comprising:
determining equipment resources required for operating the super-resolution generation countermeasure network model according to the type of the equipment;
initializing the equipment resource interface;
the generation of the confrontation network model by utilizing the target super-resolution to process each picture in the picture set to be processed comprises the following steps:
and calling equipment resources through the equipment resource interface to run the target super-resolution to generate a confrontation network model, and processing each picture in the picture set to be processed.
4. The method of claim 1, wherein the device comprises a display screen;
the method further comprises the following steps:
and displaying the target super-resolution in the equipment display screen to generate a target display picture corresponding to a first picture currently processed by a confrontation network model, wherein the definition of the target display picture is different from that of the first picture.
5. The method of claim 4, wherein prior to displaying in the device display screen a target display picture corresponding to the first picture currently processed by the target super resolution generation countermeasure network model, further comprising:
determining the definition of a target display picture corresponding to each picture in the picture set according to the number of the pictures in the picture set to be processed and the processing sequence of each picture, wherein the definition of the target display picture corresponding to different pictures is different;
and according to the definition of a target display picture corresponding to the first picture, carrying out definition processing on the first picture to generate the target display picture.
6. The method of claim 4, wherein after determining the performance of the device, further comprising:
displaying, in the display screen, a performance of the device.
7. The method of any of claims 1-6, wherein prior to determining the performance of the device, further comprising:
determining the target super-resolution generation confrontation network model, and processing time of each picture when each picture is subjected to sharpening processing;
the determining the performance of the device comprises:
determining a signal-to-noise ratio difference value accumulated value according to the difference value between the peak signal-to-noise ratio of the corresponding clarified picture and the peak signal-to-noise ratio of each picture in the picture set;
and determining the frame number of the pictures of the equipment transmitted per second according to the processing time of each picture in the picture set.
8. An apparatus performance testing device, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring a target super-resolution to generate a confrontation network model and a picture set to be processed, and the picture set comprises a plurality of pictures;
the processing module is used for generating a confrontation network model by utilizing the target super-resolution, processing each picture in the to-be-processed picture set and acquiring a sharpening picture corresponding to each picture;
and the first determining module is used for determining the performance of the equipment according to the peak signal-to-noise ratio of each picture and the peak signal-to-noise ratio of the sharpening picture corresponding to each picture.
9. An electronic device, comprising: memory, processor and program stored on the memory and executable on the processor, characterized in that the processor implements the device performance testing method according to any of claims 1-7 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method for device performance testing according to any one of claims 1 to 7.
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