Image prediction method, device and storage medium

Abstract

The present invention discloses an image prediction method, device and storage medium, which belong to the field of deep learning technology. The method comprises: obtaining an image to be tested; inputting the image to be tested into a preset neural network model to obtain a predicted category of the image to be tested, wherein the weight of each layer of the preset neural network model is obtained through layered adaptive learning rate training. The technical solution of the present invention, by inputting the acquired image to be tested into a preset neural network model, since the weight of each layer of the preset neural network model is obtained through layered adaptive learning rate training, the weight of each layer is more accurate, so that the determined preset neural network model is more accurate, so when the image to be tested is input into the preset neural network, the predicted category of the image to be tested can be accurately obtained.

Description

Technical Field

[0001] The present invention relates to the field of deep learning technology, and in particular to an image prediction method, device and storage medium. Background Art

[0002] At present, deep learning models, especially neural networks, have been widely used. The training process of neural network models is the most time-consuming process in building a practical network. In order to improve training efficiency and shorten training time, the current artificial intelligence (AI) platform provides users with a multi-GPU parallel training process. However, in the multi-GPU parallel training process, in order to improve resource utilization, the batch size on each GPU will be increased accordingly. When the batch size is relatively large, it will affect the accuracy of the model. In addition, in the existing neural model training process, a globally unique learning rate is usually used to determine the weight of each layer, thereby affecting the accuracy of the model. Therefore, the neural network model obtained by the existing training method will significantly affect the prediction accuracy of the image during image prediction. Summary of the invention

[0003] The main purpose of the embodiments of the present invention is to propose an image prediction method, device and storage medium, aiming to achieve accurate image prediction by obtaining a preset neural network model through hierarchical adaptive learning rate training.

[0004] To achieve the above-mentioned purpose, an embodiment of the present invention provides an image prediction method, which includes: obtaining an image to be tested; inputting the image to be tested into a preset neural network model to obtain a predicted category of the image to be tested, wherein the weight of each layer of the preset neural network model is obtained through layered adaptive learning rate training.

[0005] To achieve the above-mentioned purpose, an embodiment of the present invention further proposes an image prediction device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program implements the above-mentioned method when executed by the processor.

[0006] To achieve the above objectives, the present invention provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above method.

[0007] The image prediction method, device and storage medium proposed in the present invention input the acquired image to be tested into a preset neural network model. Since the weight of each layer of the preset neural network model is obtained through layered adaptive learning rate training, the weight of each layer is more accurate, so that the determined preset neural network model is more accurate. Therefore, when the image to be tested is input into the preset neural network, the predicted category of the image to be tested can be accurately obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a flowchart of an image prediction method provided by Embodiment 1 of the present invention;

[0009] Figure 2 is a flowchart of an image prediction method provided by Embodiment 2 of the present invention;

[0010] Figure 3 This is a training process operation interaction diagram provided by Embodiment 2 of the present invention;

[0011] Figure 4 is a flow chart of step S220 of the second image pre-storage method provided by the present invention;

[0012] Figure 5 is a schematic diagram of the dynamic change of the weight attenuation parameter provided in the second embodiment of the present invention;

[0013] Figure 6 is a flowchart of an image prediction method provided by Embodiment 3 of the present invention;

[0014] Figure 7 It is a structural block diagram of an image prediction device provided by Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0015] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0016] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present invention, and have no special meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0017] Embodiment 1

[0018] like Figure 1 As shown, this embodiment provides a flowchart of an image prediction method, including:

[0019] Step S110: Acquire the image to be tested.

[0020] Among them, the image to be tested can be obtained by shooting or by screening from a database. The specific method of obtaining the image to be tested is not limited in this embodiment, and the purpose of image prediction can be to determine the category of the image to be tested, for example, to determine whether the image to be tested is an animal, a landscape, a building or a person, etc. Of course, this embodiment is only an example and does not limit the specific content of the category.

[0021] It should be noted that after acquiring the image to be tested, in order to make the prediction result more accurate, the image to be tested can be preprocessed before inputting the preset model, wherein the preprocessing specifically includes image denoising, image enhancement or image filling, etc., so as to eliminate interference factors in the image to be tested, thereby making the prediction result more accurate.

[0022] Step S120: inputting the image to be tested into a preset neural network model to obtain a predicted type of the image to be tested.

[0023] Among them, the weight of each layer of the preset neural network model is obtained through layered adaptive learning rate training. Layered adaptive learning rate training refers to setting a matching learning rate for each layer in the process of training the neural network through samples, and when calculating the weight of each layer, the learning rate corresponding to the layer is used for determination. Therefore, compared with the prior art method of using a globally unique learning rate for all layers to determine the weights of each layer, the weight determination is more accurate, and therefore the determined preset neural network model is more accurate.

[0024] In one example, inputting the image to be tested into a preset neural network model to obtain a predicted category of the image to be tested may include: inputting the image to be tested into a preset neural network model to obtain a type probability set, wherein the type probability set contains a correspondence between each category and a probability value; determining the category corresponding to the largest probability value in the probability set; and using the category corresponding to the largest probability value as the predicted category of the image to be tested.

[0025] For example, the image to be tested 1 is input into the preset neural network model, and the image to be tested 1 contains a cat. The input image to be tested 1 is predicted by the preset neural network model, and a type probability set {animal 98%, person 1%, scenery 1%} is obtained respectively. Through the type probability set, it can be concluded that the maximum probability value is 98%, and the category corresponding to the maximum probability value is animal, so that it can be determined that the predicted category of the image to be tested is animal.

[0026] In one example, before inputting the image to be tested into the preset neural network model, the method may further include: using multiple graphics processors GPU to train the sample images in parallel to obtain the preset neural network model.

[0027] In one example, using multiple graphics processing units (GPUs) to train sample images in parallel to obtain the preset neural network model can include: each GPU determines the initial gradient of each layer of the initial neural network model according to the sample image; obtaining the aggregated gradient and layer learning rate of each layer of the initial neural network model according to the initial gradient of each layer; determining the preset weight of each layer according to the current weight of each layer, the layer learning rate, the weight decay parameter and the global learning rate of the initial neural network model; and obtaining the preset neural network model according to the preset weight of each layer.

[0028] The image prediction method provided in the implementation of the present application is implemented by inputting the acquired image to be tested into a preset neural network model. Since the weight of each layer of the preset neural network model is obtained through layered adaptive learning rate training, the weight of each layer is more accurate, and thus the determined preset neural network model is more accurate. Therefore, when the image to be tested is input into the preset neural network, the predicted category of the image to be tested can be accurately obtained.

[0029] Embodiment 2

[0030] Figure 2 This is a flowchart of an image prediction method provided by Embodiment 2 of the present invention. This embodiment is based on the above embodiment. In this embodiment, before the image to be tested is input into the preset neural network model, it also includes: using multiple graphics processors GPU to train the sample images in parallel to obtain the preset neural network model. Accordingly, the method of this embodiment specifically includes the following operations:

[0031] Step S210: Acquire an image to be tested.

[0032] Step S220: using multiple graphics processors GPU to train sample images in parallel to obtain a preset neural network model.

[0033] It should be noted that, in this embodiment, before the image to be tested is input into the preset neural network model, it is necessary to train the preset neural network model through sample images. The artificial intelligence (AI) platform involves four applications, namely, application manager, resource manager, job scheduler and executor, and the training of the preset neural network model is realized through the operation interaction between the four applications. Figure 3 Shown is an interactive graph of a training run:

[0034] Among them, the application manager runs on a high performance computing (HPC) cluster. When receiving a model training job submitted by a user, it will send a resource request to the resource management module; the resource manager will allocate corresponding resources on the HPC cluster for the training job submitted by the user according to the resource request, such as GPU, and send a successful application instruction to the application manager; when the application manager obtains the successful application instruction, it will send a start job instruction to the job scheduler, wherein each training task corresponds to a job scheduler; after receiving the start job instruction, the job scheduler will send a start instruction to the executor, and the executor is responsible for executing the training task of the neural network model assigned to each node in the cluster, and when the training is completed, it will send a training job completion instruction to the application manager through the job scheduler; when the application manager receives the training job completion, it will send a release resource instruction to the resource manager, so that the resource manager can recycle the allocated resources. The process of executing data parallel training by the executor corresponds to step S220 of this embodiment, so the process of step S220 is specifically described below.

[0035] like Figure 3 As shown, step S220 specifically includes the following steps:

[0036] Step S221: Each GPU determines the initial gradient of each layer of the initial neural network model based on the sample image.

[0037] In one example, before each GPU determines the initial gradient of each layer of the initial neural network model according to the sample image, it may also include: obtaining model building instructions, and generating an original neural network model on each GPU according to the model building instructions; determining the parameters of the original neural network model contained on the specified GPU, and using the original neural network model contained on the specified GPU as the initial neural network model; broadcasting the parameters of the original neural network model contained on the specified GPU to the remaining GPUs, so that the remaining GPUs update the parameters of their own original neural network models according to the broadcasted parameters to obtain the initial neural network model.

[0038] In a specific implementation, the allocated resources are three GPUs, that is, three GPUs are used for parallel training. When the model building instruction is obtained, the original neural network model is generated on each GPU according to the model building instruction. Since the parameters of the original neural network model are randomly generated on each GPU, the parameters of each original neural network model generated are definitely different. In order to maintain consistency, a GPU can be specified. For example, each GPU has a number, and the original neural network model contained in the GPU numbered 0 is specified as the initial neural network model, and the parameters of the original neural network model contained in the GPU numbered 0 are broadcast to the remaining two GPUs. The remaining two GPUs will update the parameters of their own original neural network models according to the broadcast parameters, so that each GPU contains an initial neural network model with the same parameters.

[0039] In this embodiment, the sample image is read on each GPU, and the operation of the initial neural network model is performed to obtain the initial gradient of each layer of the initial neural network model of each GPU. For example, for the first layer of the initial neural network model, the current weight corresponding to the layer is determined to be w l , where l represents the layer number, in this case l is 1, and the first GPU determines the initial gradient as D1(w 1 ), the second GPU calculates the initial gradient to be D2(w 1 ), the initial gradient is determined by calculation on the third GPU as D3(w 1 ).

[0040] Step S222: Obtain the aggregated gradient and layer learning rate of each layer of the initial neural network model according to the initial gradient of each layer.

[0041] In one example, obtaining the aggregated gradient and layer learning rate of each layer of the initial neural network model according to the initial gradients of each layer may include: adding the initial gradients of each layer to obtain the gradient sum of each layer; taking the ratio of the gradient sum of each layer to the number of initial gradients of each layer as the aggregated gradient of each layer; and obtaining the layer learning rate of each layer according to the aggregated gradient of each layer.

[0042] In one example, obtaining the layer learning rate of each layer according to the aggregated gradient of each layer includes: determining the upper boundary value and the lower boundary value of the learning rate, and the ratio of the current weight of each layer to the aggregated gradient; when it is determined that the ratio of the current weight to the aggregated gradient is between the lower boundary value and the upper boundary value, the ratio of the current weight to the aggregated gradient is used as the layer learning rate of each layer; when it is determined that the ratio of the current weight to the aggregated gradient is greater than the upper boundary value, the upper boundary value is used as the layer learning rate of each layer; when it is determined that the ratio of the current weight to the aggregated gradient is less than the lower boundary value, the lower boundary value is used as the layer learning rate of each layer.

[0043] Specifically, in this embodiment, for the first layer in the initial neural network model, after determining that the first GPU calculates the initial gradient D1(w 1 ), the second GPU calculates the initial gradient to be D2(w 1 ), the initial gradient is determined by calculation on the third GPU as D3(w 1 ), the ratio of the gradient of each layer to the number of initial gradients of each layer can be used as the aggregation gradient of each layer. Then the aggregation gradient for the first layer is Of course, this embodiment only takes the first layer as an example for illustration, and the method of determining the aggregation gradient for other layers is roughly the same, so it will not be repeated in this embodiment.

[0044] After obtaining the aggregate gradient of each layer, the following formula (1) can be used to obtain the layer learning rate of each layer:

[0045]

[0046] Among them, λ l is the layer learning rate of layer l in the initial neural network model, l is the layer number, η is the expansion coefficient of the weight gradient ratio, ε is optional, and w l is the current weight of layer l, is the aggregation gradient of layer l, T m is the upper limit of the learning rate, T n is the lower boundary value of the learning rate.

[0047] It should be noted that η, ε, T m and T n It needs to be set before training, and the specific value can be limited by the user according to the actual situation. The value size is not limited in this implementation.

[0048] The meaning of the clip formula is: When the ratio is q, when T m <q<T n When , the layer learning rate λ l =q; when q<T m When , the layer learning rate λ l =T m ; When q>T n When λ l =T nTherefore, through the clip operation, the layer learning rate can be always controlled between the upper and lower boundary values, and it will not be too large. And by defining the layer learning rate for each layer, the initial neural network model parameter update is more efficient and reasonable, which accelerates the training process.

[0049] Step S223: Determine the preset weight of each layer according to the current weight of each layer, the layer learning rate, the weight decay parameter and the global learning rate of the initial neural network model.

[0050] After obtaining the layer learning rate of each layer, the preset weight of each layer can be determined by the following formula (2):

[0051]

[0052] in, represents the preset weight of layer l, λ l is the layer learning rate of layer l in the initial neural network model, t represents the current number of iterations, is the current weight of layer l, γ is the global learning rate, is the aggregated gradient of layer l, and β is the weight decay parameter. And from formula (2), it can be concluded that the learning rate and weight decay parameter can be adjusted separately, thus achieving the decoupling of the two.

[0053] It should be noted that the weight attenuation parameter β in this embodiment is dynamically changed, and a 1Cycle adjustment strategy is specifically applied, such as Figure 5 The figure shows a schematic diagram of the dynamic change of the weight decay parameter determined by the 1Cycle adjustment strategy. It can be seen from the figure that the weight decay parameter β increases linearly from 0.0005 to 0.01 in the first 13 iterations of training, and then decreases linearly from 0.01 to 0.0005 in the next 14 iterations, and remains constant at 0.0005 in the last iteration. Of course, this implementation is only an example, and does not limit the critical number of iterations of the change and the number of iteration terminations. Users can limit them according to actual conditions.

[0054] Step S224: Obtain a preset neural network model according to the preset weights of each layer.

[0055] Among them, by cyclically executing the above steps S221 to S223 until the set number of iterations is reached, in the final number of iterations, when the preset weights of each layer have been determined, the parameters of each layer of the preset neural network model are known, and thus the preset neural network model is obtained according to the determined parameters.

[0056] Step S230: input the image to be tested into a preset neural network model to obtain a predicted type of the image to be tested.

[0057] The image prediction method provided in the implementation of the present application is to input the acquired image to be tested into a preset neural network model. Since the weight of each layer of the preset neural network model is obtained through layered adaptive learning rate training, the weight of each layer is more accurate, so the preset neural network model determined is more accurate. Therefore, when the image to be tested is input into the preset neural network, the predicted category of the image to be tested can be accurately obtained. In addition, during the neural network training process, since the layer learning rate can be determined for each layer, and the decoupling of the learning rate and the weight decay parameter is achieved, the training process can be made more efficient during ultra-large Batch Size training. Since the sample Batch Size processed on each GPU is larger, the overall resource utilization is further improved.

[0058] Embodiment 3

[0059] Figure 6 A flowchart of an image prediction method provided for Embodiment 3 of the present invention. This embodiment is based on the above embodiment. In this embodiment, after the image to be tested is input into a preset neural network model to obtain a predicted category of the image to be tested, it also includes: detecting the prediction result and issuing an alarm prompt when it is determined that the prediction result is abnormal.

[0060] Step S310: Acquire the image to be tested.

[0061] Step S320: input the image to be tested into a preset neural network model to obtain a predicted type of the image to be tested.

[0062] Step S330: testing the prediction results.

[0063] Specifically, when detecting the prediction results, it may be specifically detected whether there are obvious errors in the prediction results, for example, the prediction results may be identified to determine whether there are garbled characters or empty contents.

[0064] Step S340: issuing an alarm if the prediction result is determined to be abnormal.

[0065] If the prediction result is determined to be garbled or the content is empty, the prediction result is determined to be abnormal. In the case of abnormal prediction result, an alarm prompt will be issued. The alarm prompt can be in the form of text prompt, voice prompt or light prompt. For example, if the prediction result is determined to be garbled, a voice prompt "prediction result error, please check" will be issued. Since the cause of the failure may be equipment failure, communication terminal or neural network model parameter configuration error, by issuing an alarm prompt, the user can be notified to take corresponding measures in time, such as replacing the equipment or re-predicting by adjusting the parameter configuration when the equipment is determined to be normal.

[0066] The image prediction method provided by the implementation of this application is to input the acquired image to be tested into a preset neural network model. Since the weight of each layer of the preset neural network model is obtained through layered adaptive learning rate training, the weight of each layer is more accurate, so the preset neural network model determined is more accurate. Therefore, when the image to be tested is input into the preset neural network, the predicted category of the image to be tested can be accurately obtained. By detecting the prediction results and issuing an alarm prompt when the prediction results are abnormal, the user is prompted to perform equipment maintenance in a timely manner to further improve the accuracy of the prediction results.

[0067] Embodiment 4

[0068] like Figure 7 As shown, the fourth embodiment of the present invention proposes an image prediction device, which includes a memory 720, a processor 710, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor 710 and the memory 720. When the program is executed by the processor, the image prediction method in the embodiment of the present invention is realized:

[0069] Acquire an image to be tested; input the image to be tested into a preset neural network model to obtain a predicted category of the image to be tested, wherein the weight of each layer of the preset neural network model is obtained through hierarchical adaptive learning rate training.

[0070] The processor 710 and the memory 720 in the terminal may be connected via a bus or other means. Figure 7 The example of connecting through bus is taken in the following.

[0071] The memory 720, as a computer-readable storage medium, may be configured to store software programs, computer executable programs, and modules, such as program instructions / modules corresponding to the time domain resource determination method in the embodiment of the present application. The memory 720 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the device, etc. In addition, the memory 720 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 720 may further include a memory remotely arranged relative to the processor 710. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0072] Embodiment 5

[0073] Embodiment 5 of the present invention provides a readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the image prediction method in the embodiment of the present invention:

[0074] Acquire an image to be tested; input the image to be tested into a preset neural network model to obtain a predicted category of the image to be tested, wherein the weight of each layer of the preset neural network model is obtained through hierarchical adaptive learning rate training.

[0075] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0076] In hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0077] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the present invention is not limited thereby. Any modification, equivalent substitution and improvement made by those skilled in the art without departing from the scope and essence of the present invention shall be within the scope of the present invention.

[0078] By way of exemplary and non-limiting examples, a detailed description of exemplary embodiments of the present application has been provided above. However, various modifications and adjustments to the above embodiments will be apparent to those skilled in the art when considered in conjunction with the accompanying drawings and claims, but will not depart from the scope of the present invention. Therefore, the proper scope of the present invention will be determined according to the claims.

Claims

1. An image prediction method, characterized in that: include: Acquire the image to be tested; Multiple graphics processors (GPUs) are used to train sample images in parallel to obtain a preset neural network model; Inputting the image to be tested into a preset neural network model to obtain a predicted category of the image to be tested, wherein the weight of each layer of the preset neural network model is obtained through layered adaptive learning rate training; The method of using multiple graphics processors (GPUs) to train sample images in parallel to obtain a preset neural network model includes: Each GPU determines an initial gradient of each layer of the initial neural network model according to the sample image; Obtaining the aggregated gradient of each layer of the initial neural network model according to the initial gradient of each layer; Determine the upper and lower bounds of the learning rate and the ratio of the current weight of each layer to the aggregated gradient; When it is determined that the ratio of the current weight to the aggregated gradient is between the lower boundary value and the upper boundary value, the ratio of the current weight to the aggregated gradient is used as the layer learning rate of each layer; When it is determined that the ratio of the current weight to the aggregated gradient is greater than the upper boundary value, the upper boundary value is used as the layer learning rate of each layer; When it is determined that the ratio of the current weight to the aggregated gradient is less than the lower boundary value, the lower boundary value is used as the layer learning rate of each layer; Determining a preset weight for each layer according to the current weight of each layer, the layer learning rate, a weight decay parameter, and the global learning rate of the initial neural network model; The preset neural network model is obtained according to the preset weights of each layer.

2. The method according to claim 1, characterized in that Before each GPU determines the initial gradient of each layer of the initial neural network model according to the sample image, the method further includes: Obtaining a model building instruction, and generating an original neural network model on each of the GPUs according to the model building instruction; Determine the parameters of the original neural network model contained in the specified GPU, and use the original neural network model contained in the specified GPU as the initial neural network model; The parameters of the original neural network model contained in the designated GPU are broadcast to the remaining GPUs, so that the remaining GPUs update the parameters of their own original neural network models according to the broadcasted parameters to obtain the initial neural network model.

3. The method according to claim 1, characterized in that The step of obtaining the aggregate gradient of each layer of the initial neural network model according to the initial gradient of each layer comprises: Adding the initial gradients of each layer to obtain the gradient sum of each layer; The ratio of the gradient sum of each layer to the number of initial gradients of each layer is taken as the aggregate gradient of each layer.

4. The method according to claim 1, characterized in that: The step of inputting the image to be tested into a preset neural network model to obtain a predicted category of the image to be tested includes: Inputting the image to be tested into the preset neural network model to obtain a type probability set, wherein the type probability set contains a corresponding relationship between each category and a probability value; Determine the category corresponding to the maximum probability value in the probability set; The category corresponding to the maximum probability value is used as the predicted category of the image to be tested.

5. The method according to claim 4, characterized in that After inputting the image to be tested into a preset neural network model to obtain the predicted category of the image to be tested, the method further includes: Testing the prediction results; When it is determined that the prediction result is abnormal, an alarm is issued.

6. A device for image prediction, characterized in that: The device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the image prediction method as described in any one of claims 1 to 5 are realized.

7. A storage medium for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the image prediction method according to any one of claims 1 to 5.

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