WO2022247112A1 - Procédé et appareil de traitement de tâche, dispositif, support d'enregistrement, programme informatique et produit programme - Google Patents

Procédé et appareil de traitement de tâche, dispositif, support d'enregistrement, programme informatique et produit programme Download PDF

Info

Publication number
WO2022247112A1
WO2022247112A1 PCT/CN2021/124996 CN2021124996W WO2022247112A1 WO 2022247112 A1 WO2022247112 A1 WO 2022247112A1 CN 2021124996 W CN2021124996 W CN 2021124996W WO 2022247112 A1 WO2022247112 A1 WO 2022247112A1
Authority
WO
WIPO (PCT)
Prior art keywords
panorama
units
unit
attributes
operation unit
Prior art date
Application number
PCT/CN2021/124996
Other languages
English (en)
Chinese (zh)
Inventor
徐子豪
李韡
杨凯
高原
吴立威
崔磊
Original Assignee
上海商汤智能科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 上海商汤智能科技有限公司 filed Critical 上海商汤智能科技有限公司
Publication of WO2022247112A1 publication Critical patent/WO2022247112A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
    • G06F3/0481—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
    • G06F3/0482—Interaction with lists of selectable items, e.g. menus
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
    • G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
    • G06F3/0487—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser
    • G06F3/0488—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures
    • 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

Definitions

  • the present disclosure relates to the technical field of artificial intelligence, and relates to but not limited to task processing methods, devices, equipment, storage media, computer programs and program products.
  • Embodiments of the present disclosure provide a task processing method, device, device, storage medium, computer program, and program product.
  • an embodiment of the present disclosure provides a task processing method, including:
  • the first panorama includes at least two operation units and resource units corresponding to each of the operation units;
  • the attributes of each operation unit in the intermediate file include attributes of resource units that have an input relationship and/or an output relationship with each of the operation units;
  • the intermediate file is converted into a second panorama.
  • the acquiring the first panorama of the task to be processed includes: determining a plurality of operation units and a plurality of resource units for implementing the task to be processed; each operation unit is used to implement a depth The training function, evaluation function or reasoning function in the learning model; each said resource unit includes the data input and/or output by the corresponding operation unit during the execution of the processing operation; the operation unit based on the task to be processed and the The multiple resource units are used to construct the first panorama of the task to be processed in the canvas of the model training platform.
  • the task processing method is limited to the multi-model serial training process of complex scenes, which is more convenient and effective than the existing method that can only manually control the operation of a single algorithm module. Quickly build a full-chain algorithm solution.
  • converting the first panorama into an intermediate file based on the attributes of the at least two operation units and the attribute of at least one resource unit corresponding to each operation unit includes: Determine the connection relationship between the at least two operating units; based on the attributes of the at least two operating units and the attributes of at least one resource unit corresponding to each of the operating units, each of the operating units will have an input The attribute of the resource unit of the relationship or the output relationship is merged into the attribute of the corresponding operation unit; the connection relationship between the at least two operation units and the attributes of the merged operation unit are saved to obtain the intermediate file.
  • the attributes of the input resource unit or output resource unit of each operation unit are combined into the attributes of the corresponding operation unit, and the connection relationship of each operation unit is calculated at the same time, so as to store each operation
  • the properties of the unit and the connection relationship between each operating unit are used as an intermediate file, which can conveniently store the content of the first panorama and provide support for subsequent conversion of other functional maps.
  • the saving the connection relationship between the at least two operation units and the attributes of the merged operation units to obtain the intermediate file includes: storing the at least two operation units The connection relationship between the units is incorporated into the attributes of the corresponding operation units; the merged attributes of at least two operation units are saved to obtain the intermediate file.
  • connection relationship of each operation unit is further incorporated into the attributes of the corresponding operation unit, and stored as an intermediate file, which can connect the first panorama and meet the needs of conversion into other pictures, solving the problem of the first panorama Difficult translation issues to second panoramas that are runnable on the backend.
  • the determining the connection relationship between the at least two operation units includes: in response to each of the resource units simultaneously serving as an output of the first operation unit and an input of the second operation unit, It is determined that there is a connection relationship between the first operation unit and the second operation unit.
  • the converting the intermediate file into a second panorama includes: determining an intermediate result graph corresponding to the intermediate file; responding to the fact that the intermediate result graph is a directed acyclic graph Next, the second panorama is generated based on the at least two operating units and the preset workflow template corresponding to each of the operating units; wherein, the preset workflow template corresponding to each of the operating units is the The above-mentioned front end is set according to the target task.
  • the determining the intermediate result graph corresponding to the intermediate file includes: extracting the connection relationship between each operation unit in the intermediate file and each operation unit; according to the connection relationship, Connecting each operation unit in the intermediate file to obtain the intermediate result graph.
  • an intermediate result graph is formed based on each operation unit in the intermediate file and the connection relationship between each operation unit, which can provide support for subsequent conversion of other function graphs.
  • the generating the second panorama based on the at least two operating units and the preset workflow template corresponding to each operating unit includes: All operation units are topologically sorted to obtain linear arrangement results; key information of each operation unit extracted from the intermediate result map is obtained; wherein, the key information is configured to fill necessary fields in the second panorama ; Based on the key information, fill in the necessary fields in the preset workflow template corresponding to the operation unit in turn; according to the linear arrangement result, based on the preset workflow template corresponding to each of the operation units, generate the Second panorama.
  • topologically sort the operation units in the intermediate file and extract key information from all operation units to overwrite the content of the corresponding preset workflow template, and finally generate the second panorama according to the topological sort order, which can be directly scheduled by the back-end task tool call.
  • the converting the intermediate file into a second panorama further includes: in response to the fact that the intermediate result graph is not a directed acyclic graph, outputting an error in creating an intermediate result graph Prompt information.
  • an embodiment of the present disclosure provides a task processing device, including:
  • An acquisition module configured to acquire a first panorama of tasks to be processed; wherein, the first panorama includes at least two operation units and resource units corresponding to each of the operation units;
  • the first conversion module is configured to convert the first panorama into an intermediate file, the attributes of each operation unit in the intermediate file include resource units having an input relationship and/or an output relationship with each operation unit Attributes;
  • the second conversion module is configured to convert the intermediate file into a second panorama.
  • an embodiment of the present disclosure provides an electronic device, including a memory and a processor, the memory stores a computer program that can run on the processor, and the processor implements the above-mentioned task processing method when executing the program. A step of.
  • an embodiment of the present disclosure provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above-mentioned task processing method are implemented.
  • the embodiment of the present disclosure further provides a computer program, including computer readable code, when the computer readable code is run in the electronic device, the processor in the electronic device executes the program for realizing the above task processing method.
  • the embodiments of the present disclosure further provide a computer program product, the computer program product includes one or more instructions, and the one or more instructions are suitable for being loaded by a processor and executing the steps in the above task processing method .
  • the first panorama of the task to be processed is obtained; wherein, the first panorama includes at least two operation units and resource units corresponding to each of the operation units; then, the obtained The first panorama is converted into an intermediate file, and the attributes of each operation unit in the intermediate file include the attributes of resource units that have an input relationship and/or an output relationship with each of the operation units; finally, the intermediate file is converted It is the second panorama; in this way, by defining an intermediate file that can connect the first panorama and meet the needs of conversion into other pictures, and open up the conversion from the first panorama to the back-end second panorama, complex scenes can be realized
  • the multi-model series training process is more convenient and faster to build a full-chain algorithm solution than the existing technology that can only use a single algorithm module.
  • FIG. 1 is a schematic flowchart of a task processing method provided by an embodiment of the present disclosure
  • FIG. 2 is a schematic flowchart of a task processing method provided by an embodiment of the present disclosure
  • FIG. 3 is a schematic flowchart of a task processing method provided by an embodiment of the present disclosure
  • FIG. 4 is a schematic flowchart of a task processing method provided by an embodiment of the present disclosure.
  • FIG. 5A is a schematic diagram of a first panoramic view provided by an embodiment of the present disclosure.
  • FIG. 5B is a schematic flow diagram of converting the first panorama image into an intermediate result image provided by an embodiment of the present disclosure
  • FIG. 5C is a schematic flow chart of converting an intermediate result map into a second panorama provided by an embodiment of the present disclosure
  • FIG. 5D is a schematic diagram of an intermediate result graph provided by an embodiment of the present disclosure.
  • FIG. 5E is a schematic diagram of the linear arrangement results of each operating unit provided by the embodiment of the present disclosure.
  • FIG. 5F is a schematic diagram of a second panorama provided by an embodiment of the present disclosure.
  • FIG. 6 is a schematic diagram of the composition and structure of a task processing device provided by an embodiment of the present disclosure.
  • FIG. 7 is a schematic diagram of a hardware entity of an electronic device provided by an embodiment of the present disclosure.
  • first ⁇ second ⁇ third involved in the embodiments of the present disclosure is only to distinguish similar objects, and does not represent a specific ordering of objects. Understandably, “first ⁇ second ⁇ third "Where permitted, the preset order or sequence may be interchanged so that the embodiments of the disclosure described herein can be practiced in an order other than that illustrated or described herein.
  • Artificial Intelligence is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.
  • artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can respond in a similar way to human intelligence.
  • Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that the machines have the functions of perception, reasoning and decision-making.
  • Artificial intelligence technology is a comprehensive subject that involves a wide range of fields, including both hardware-level technology and software-level technology.
  • Artificial intelligence basic technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation/interaction systems, and mechatronics.
  • Artificial intelligence software technology mainly includes several major directions such as computer vision technology, speech processing technology, natural language processing technology, and machine learning/deep learning.
  • Machine Learning is a multi-field interdisciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. Specializes in the study of how computers simulate or implement human learning behaviors to acquire new knowledge or skills, and reorganize existing knowledge structures to continuously improve their performance.
  • Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent, and its application pervades all fields of artificial intelligence.
  • Machine learning and deep learning usually include techniques such as artificial neural network, belief network, reinforcement learning, transfer learning, inductive learning, and teaching learning.
  • An embodiment of the present disclosure provides a task processing method, which is applied to an electronic device.
  • An end-to-end visualization model generation platform is deployed on the electronic device, and a general training framework for artificial intelligence models commonly used in visual fields such as object detection and image classification is embedded.
  • the electronic devices include but are not limited to notebook computers, tablet computers, multimedia devices, mobile Internet devices or other types of devices, as well as devices with computing capabilities such as servers and distributed computing nodes.
  • the function realized by the method can be realized by calling the program code by the processor in the electronic device, and of course the program code can be stored in the computer storage medium.
  • Fig. 1 is a schematic flowchart of a task processing method provided by an embodiment of the present disclosure. As shown in Fig. 1, the method at least includes the following steps:
  • Step S110 acquiring the first panorama of the task to be processed
  • the first panorama (raw graph) is a complete solution for generating artificial intelligence models built by users on the canvas, including functions such as model training, evaluation, and reasoning logic series.
  • the canvas is a section on the artificial intelligence training platform for users to drag and drop different components to build the whole process of model production.
  • the first panorama includes at least two operation units (operation, op) and a resource unit corresponding to each operation unit.
  • the storage file of the first panorama includes attributes of at least two operation units and attributes of at least two resource units.
  • each operation unit is a virtualized node after encapsulating an algorithm module
  • the resource unit can be represented as a data node (node) having an input relationship and/or an output relationship with the operation unit
  • each resource unit is A virtualization node after encapsulating a data processing module, and the data processing module provides input data for a certain algorithm module, or processes output data of another algorithm module.
  • a resource unit is an input of an operation unit; in some embodiments, a resource unit is an output of an operation unit; in other embodiments, a resource unit is an output of an operation unit; The resource unit is both the output of the previous operation unit and the input of the next operation unit.
  • the first panorama is stored in the artificial intelligence training platform in the form of a file.
  • the first panorama includes the attributes of the operation unit and the attributes of the resource unit; in other implementations, the first panorama includes the attributes of the operation unit, the attributes of the resource unit, and the attributes of the operation unit and Connection relationship between resource units, such as connection line (link).
  • the attributes of the operation unit may include functions such as training, reasoning, and evaluation of the corresponding algorithm module, and may also include the name of the resource unit connected to the operation unit.
  • the attributes of the resource unit may include data entities in the process of model training or reasoning, and may also include data set interface functions, formats of input and output data, image sizes, and so on.
  • Step S120 converting the first panorama into an intermediate file
  • the intermediate file is a set intermediate storage form of a graph
  • the attributes of each operation unit in the intermediate file include attributes of resource units that have an input relationship and/or an output relationship with each operation unit .
  • a possible implementation is, for all operating units in the first panoramic view, the attributes of the input resource unit or output resource unit of each operating unit are incorporated into the attributes of the corresponding operating unit; at the same time, based on the The connection relationship between each operation unit, determine the connection relationship between two operation units that have an input and output relationship with the same resource unit, save the attributes of all operation units in the first panorama and the connection between every two operation units relationship to get the converted intermediate file.
  • the converted intermediate file can conveniently store the content of the first panorama, and provide support for subsequent conversion of other functional maps.
  • a possible implementation is, for all operating units in the first panoramic view, the attributes of the input resource unit or output resource unit of each operating unit are incorporated into the attributes of the corresponding operating unit; at the same time, based on the The connection relationship between each operation unit determines the connection relationship between two operation units that have an input and output relationship with the same resource unit, and incorporates the connection relationship into the attributes of the corresponding operation unit, directly storing the attributes of all operation units , to get the converted intermediate file.
  • the converted intermediate file can conveniently store the content of the first panorama, can connect the first panorama and can meet the needs of conversion into other pictures, and solves the difficulty of converting the first panorama to the second panorama that can be run at the back end. Problem with converting translations.
  • Step S130 converting the intermediate file into a second panorama.
  • the program code corresponding to the second panorama is used to be invoked by the task scheduling tool at the back end;
  • the task scheduling tool at the back end is a workflow engine (argo engine), and the second panorama (argo-workflow) corresponds to
  • the program code can be called by the back-end task scheduling tool to realize the operation of the entire algorithm scheme. That is to say, different algorithm modules and processing logics are used to form a workflow to make a unified call, so as to realize the orderly training or reasoning of multiple models in complex scenarios.
  • the intermediate file includes the properties of the operation unit, which can meet the requirement of converting to the second panorama.
  • the program code corresponding to the second panorama can be implemented using container technology
  • the second panorama can correspond to a POD
  • the POD represents a unit of deployment
  • the codes in all container images in the POD can form the first
  • code segments in different container images implement different operation units in the second panorama.
  • the container can be implemented using an open source application container engine (Docker).
  • Docker open source application container engine
  • the Docker image also contains some configuration parameters (such as anonymous volumes, environment variables, users, etc.) prepared for runtime.
  • a Docker container is the entity of the Docker image runtime. Docker containers can be created, started, stopped, deleted, paused, etc.
  • the Docker warehouse (Repository) can contain multiple warehouses; each warehouse can contain multiple tags (Tag); each tag corresponds to a Docker image, so the warehouse is used to store image files in a centralized manner.
  • the complex problem is decomposed into continuous training of the detection model and the classification model.
  • the first panorama can be constructed based on the model training task on the artificial intelligence training platform, and then the first panorama can be converted into the second panorama, for example
  • the algorithm module and the data processing module in the model training task are respectively packaged into a virtualization node to realize the operation of the entire algorithm scheme.
  • a first panorama of the task to be processed is obtained, and the first panorama includes at least two operation units and resource units corresponding to each of the operation units; then, the first The panorama is converted into an intermediate file, and the attributes of each operation unit in the intermediate file include the attributes of resource units that have an input relationship or an output relationship with each of the operation units; finally, the intermediate file is converted into a second panorama ;
  • the program code corresponding to the second panorama is used for calling by the task scheduling tool at the back end; thus, by defining an intermediate file that can connect the first panorama and meet the needs of conversion into other pictures, the first panorama can be opened
  • the conversion to the second panorama can realize the multi-model serial training process of complex scenes. Compared with the existing technology that can only use a single algorithm module, it is more convenient and faster to build a full-chain algorithm solution.
  • FIG. 2 is a schematic flowchart of a task processing method provided by an embodiment of the present disclosure. As shown in FIG. 2, the method at least includes the following steps:
  • Step S210 acquiring the first panorama of the task to be processed
  • the first panorama is a model training task diagram constructed through visual operations on the front-end interaction interface of the model training platform; multiple operation units and resource units are deployed on the front-end interaction interface, each of the The operation unit is used to implement the training function, evaluation function or inference function in the deep learning model.
  • visual operations can include clicking, inputting control instructions, uploading data, dragging and dropping algorithm modules and data processing modules, and selecting corresponding Operations such as visualization charts.
  • At least one operation unit or resource unit is determined based on the visual operation received on the front-end interaction interface of the model training platform, and a plurality of operation units and a plurality of resource units are connected to each other on the front-end interaction interface to form a first panorama of .
  • a possible implementation manner is: determine a plurality of operation units and a plurality of resource units to realize the task to be processed; each operation unit is used to realize the training function, evaluation function or reasoning function in the deep learning model; each The resource unit includes the data input and/or output by the corresponding operation unit during the execution of the processing operation; based on the operation unit of the task to be processed and the plurality of resource units, construct in the canvas of the model training platform The first panorama of the task to be processed.
  • Step S220 based on the attributes of the at least two operating units and the attributes of at least one resource unit corresponding to each of the operating units, converting the first panorama into an intermediate file;
  • Step S230 converting the intermediate file into a second panorama
  • steps S220 to S230 design the storage form of the intermediate file and convert the first panorama into the second panorama at the back end through two-step conversion, so as to realize the multi-model serial training process of complex scenes. Similar to the implementation process of step S110 to step S120, for the technical details not disclosed in the embodiment of the present disclosure, please refer to the description of the previous embodiment of the present disclosure for understanding.
  • program code corresponding to the obtained second panorama can be run by a task scheduling tool, so as to implement a multi-model series training process for complex scenes.
  • the first panorama by defining the first panorama, it can be a model training task graph, and the task processing method is limited to the multi-model serial training process of complex scenes, compared with the existing ones that can only be operated by artificially controlling a single algorithm module In this way, it is more convenient and faster to build a full-chain algorithm solution.
  • Fig. 3 is a schematic flowchart of a task processing method provided by an embodiment of the present disclosure.
  • step S120 or step S220 “based on the attributes of the at least two operating units and at least one corresponding to each of the operating units Attributes of the resource unit, "converting the first panorama into an intermediate file” includes the following steps:
  • Step S310 determining the connection relationship between the at least two operating units
  • each resource unit is connected to at least one operation unit, so that a connection relationship between two operation units connected to the same resource unit can be determined.
  • the attribute of each resource unit includes information about the operation unit connected to each resource unit, and the connection relationship between each operation unit may be determined based on the attribute of each resource unit: in response to each The resource unit serves as the output of the first operation unit and the input of the second operation unit at the same time, and it is determined that there is a connection relationship between the first operation unit and the second operation unit. In this way, based on the attributes of each resource unit, it is determined that there is a connection relationship between the two operation units connected before and after each resource unit, so that the operation units connected before and after each resource unit are connected, which is convenient for subsequent storage and composition. Different operation units are connected in series to obtain the whole algorithm scheme.
  • the first panorama can be a model training task map, and the corresponding coding form is set for each operating unit according to the actual task, and the connection between each operating unit can be determined based on the corresponding coding form of each operating unit relation.
  • the operation unit whose coding name is "model training_object detection_1” has a connection relationship with “inference_object detection_1” and "model evaluation_object detection_1” respectively.
  • model training_object detection_1 outputs the trained model A
  • model A is used as the input of the "reasoning_object detection_1” operation unit to perform inference verification on the model A
  • Model A is used as the input of the "model evaluation_object detection_1” operation unit to evaluate the ability of the model A.
  • Step S320 based on the attributes of at least two operation units and the attributes of at least one resource unit corresponding to each operation unit, incorporate the attributes of the resource units that have an input or output relationship with each of the operation units into the corresponding operation unit properties;
  • Step S330 saving the connection relationship between the at least two operation units and the attributes of the merged operation units to obtain the intermediate file
  • each operating unit and the connection relationship between each operating unit are stored as an intermediate file, which can conveniently store the content of the first panorama and provide support for subsequent conversion of other functional maps.
  • connection relationship between the at least two operating units may also be incorporated into the attributes of the corresponding operating units; the merged attributes of the at least two operating units may be saved to obtain the intermediate file.
  • connection relationship of each operation unit is further incorporated into the attributes of the corresponding operation unit, and stored as an intermediate file, which can connect the first panorama and meet the needs of conversion into other images.
  • the intermediate file can connect the first panorama and meet the needs of conversion into other images, solving the first problem.
  • Panorama to the second panorama that the backend can run is difficult to convert the translation problem.
  • Fig. 4 is a schematic flowchart of a task processing method provided by an embodiment of the present disclosure. As shown in Fig. 4, the above step S130 or step S230 "converting the intermediate file into a second panorama" can be implemented through the following steps:
  • Step S410 determining the intermediate result map corresponding to the intermediate file
  • connection relationship between each operation unit in the intermediate file and the operation units is extracted; then, according to the connection relationship, each operation unit in the intermediate file is connected to obtain the intermediate result graph.
  • An intermediate result graph is formed based on each operation unit in the intermediate file and the connection relationship between each operation unit, which can provide support for subsequent conversion of other function graphs.
  • Step S420 in response to the fact that the intermediate result graph is a directed acyclic graph, generate the second panorama based on the at least two operation units and the preset workflow template corresponding to each operation unit ;
  • the intermediate result graph is a directed acyclic graph (Directed Acyclic Graph, DAG), indicating that all operation units in the intermediate result graph each complete a part of the entire task, and each operation unit satisfies a specific execution sequence Constraints in which some units of operation must begin after execution of other units of operation has completed. In this way, it can be determined that a task composed of all operating units can be smoothly performed within a valid time.
  • DAG directed Acyclic Graph
  • the preset workflow template corresponding to each operation unit is set by the front end according to the target task. For example, for defect identification tasks in industrial scenarios, users need to detect the corresponding parts first, and then classify the corresponding different parts respectively. In this way, the problem is decomposed into continuous training of the detection model and the classification model, and the data for training the classification model depends on the inference results of the detection model. Therefore, for the scene where the target task is the defect recognition task, the front end of the model training platform reserves the detection training workflow template and the detection evaluation workflow template related to the object detection model, as well as the detection training workflow template related to the image classification model, and the detection and evaluation work. flow template.
  • the final second panorama is generated, which can realize the orderly training of multiple models in complex scenarios.
  • the process of "generating the second panorama based on the at least two operation units and the preset workflow template corresponding to each operation unit" can be realized by the following steps:
  • Step S4201 performing topological sorting on all operation units in the intermediate file to obtain a linear arrangement result
  • the linear arrangement result corresponding to the intermediate result graph can be obtained.
  • the topological sorting algorithm is mainly used to solve the dependency resolution problem in directed graphs. For any directed acyclic graph, performing topological sorting on it yields a linear arrangement of all operation units.
  • the linear permutation result satisfies the following condition: for any two operation units u and v in the graph, if there is a directed edge from u to v, then u must appear before v in the linear permutation result.
  • Step S4202 acquiring the key information of each operation unit extracted from the intermediate result graph
  • the key information is used to fill in necessary fields in the second panorama.
  • Step S4203 based on the key information, sequentially fill in the necessary fields in the preset workflow template corresponding to the operation unit;
  • Step S4204 according to the linear arrangement result, based on the preset workflow template corresponding to each of the operation units, the second panorama is generated.
  • the operation units in the intermediate file are topologically sorted, and key information is extracted from all operation units to cover the content of the corresponding preset workflow template.
  • the second panorama is generated according to the order of the topological sort, which can be directly scheduled by the back-end tasks tool call.
  • Step S430 in response to the fact that the intermediate result graph is not a directed acyclic graph, output a prompt message indicating an error in creating an intermediate result graph.
  • a prompt message is returned to stop the conversion of the intermediate file to the second panorama, so as to avoid the failure of calling the back-end task scheduling tool.
  • an intermediate result graph is first formed based on each operation unit in the intermediate file and the connection relationship between each operation unit, and then based on the operation units in the intermediate result graph that meet the conditions, the preset corresponding to each operation unit is combined.
  • the workflow template generates a second panorama that can be run by the backend, and can be directly called by the task scheduling tool of the backend.
  • this running pipeline can be described in the form of a directed acyclic graph, which can include the training and evaluation processes of various models.
  • a directed acyclic graph which can include the training and evaluation processes of various models.
  • users need to detect the corresponding components first, and then classify the corresponding different components.
  • this continuous process can build a panorama. That is to say, split the function of a task, define each function abstractly, and divide it into independent modules, and the modules are connected to each other. Modules produce task results.
  • the front end of the artificial intelligence training platform can realize the process of allowing users to manually drag and drop modules to form the first panorama. How to convert the first panorama generated by front-end drag-and-drop into the second panorama (argo-workflow graph) actually running on the back-end needs to be carefully designed.
  • the embodiment of the present disclosure proposes a method for converting the first panorama displayed on the front end into the second panorama actually run on the back end.
  • This method defines an intermediate storage form of the graph, which is mainly completed through two conversions, and finally The generated second panorama can be directly called and run by the task scheduling tool to realize the operation of the entire operation pipeline.
  • This method converts the first panorama formed by the user's front-end drag and drop to form a second panorama that can be run at the back end. The whole is divided into two stages. The first stage converts the first panorama into an intermediate result graph (inter graph ), the second stage converts the intermediate result map into a runnable second panorama.
  • a task example is used to describe the entire process.
  • the user needs to train two models of detection and classification.
  • the scenario is to detect the target to be tested first, and then classify the detected target.
  • the data for training the classification model depends on the inference results of the detection model.
  • the first panorama drawn by the user on the front end is shown in Figure 5A, where the rounded corner box is a resource unit 51, including "Dataset_Object Detection_1", “Model_Object Detection_1”, “Inference Result_Object Detection_1”, “Evaluation Report_Object Detection_1”, “Dataset_Object Detection_2”, “Dataset_Image Classification_2”, “Model_Image Classification_1”, “Inference Results_Image Classification _1” and “Evaluation Report_Image Classification_1” data processing modules, the right-angled rectangle is the operation unit 52, including "model training_object detection_1", “reasoning_object detection_1”, “model evaluation_object Detection_1
  • FIG. 5B is a schematic flow diagram of converting the first panorama image into an intermediate file according to an embodiment of the present disclosure. As shown in FIG. 5B , the flow includes the following steps:
  • Step S501 extracting attributes of all operating units from the first panorama
  • the first panorama includes resource units, operation units and connection lines.
  • the attributes of all the operating units may be extracted from the first panorama according to the storage fields corresponding to the operating units.
  • the attribute of the operation unit is the storage information of the operation unit, the connection line between the operation unit and the resource unit, and the like.
  • Step S502 merging the attribute of the resource unit having an input relationship or an output relationship with each operation unit into the attribute of the corresponding operation unit;
  • the attributes of the input and output resource units connected to each operation unit are merged into the attributes of the corresponding unit.
  • the input resource unit and the output resource unit of all the operation units are first determined through the connection line between each operation unit and at least one resource unit, and then the attributes of the input resource unit and the output resource unit are stored in the corresponding operation unit.
  • Step S503 determining the connection relationship between each operation unit
  • Step S504 storing the attributes of each operation unit as an intermediate file.
  • FIG. 5C is a schematic flow chart of converting an intermediate file into a second panorama provided by an embodiment of the present disclosure. As shown in FIG. 5C , the process includes the following steps:
  • Step S505 perform topological sorting on all operation units in the intermediate file
  • a graph is formed based on all the operation units and connection relations in the extracted intermediate file, which is the intermediate result graph, as shown in FIG. connection relationship between. Then judge whether there is a loop in the intermediate result graph, and return an error message indicating that there is an error in the created graph in response to the loop in the intermediate result graph; in response to the absence of a cycle in the intermediate result graph, use topological sorting to connect all operation units into a linear sort, and the sorting result is shown in the figure 5E, the arrangement order of each operation unit 52 is "model training_object detection_1", “model evaluation_object detection_1", “reasoning_object detection_1”, “result transfer data set_1", " Image Crop_1", “Model Training_Image Classification_1", “Model Evaluation_Image Classification_1”, “Inference_Image Classification_1".
  • Step S506 extract key information for each operation unit, and overwrite the fields of the corresponding preset template
  • Step S507 combining the preset templates of all operating units, and generating a second panorama according to the topological sorting result.
  • all operation units are integrated to form a complete second panorama according to the topological relationship, and at the same time supplement some other field information in the second panorama to generate the final second panorama picture.
  • the program code of the second panorama can be directly executed by the task scheduling tool.
  • an expression form of an intermediate graph is defined as the intermediate result representation of the panorama, which can be connected to the front-end panorama, conveniently store the content of the panorama, and provide subsequent conversion of other functional graphs such as workflow graphs and dynamic reasoning graphs. Support is provided to meet the needs of converting to other graphs.
  • the embodiment of the present disclosure aims at the problem that multiple models are required for complex scene tasks, and the training of the front and rear models has dependencies, so it is impossible to train in one step.
  • An expression form of an intermediate image design a verification and conversion algorithm to solve the problem of difficult conversion and translation from the first panorama to the second panorama that can be run on the back end.
  • the program code corresponding to the second panorama output by the conversion algorithm can be directly invoked and run by the task scheduling tool, and orderly training of multiple models in complex scenarios can be realized.
  • the present disclosure further provides a task processing device, the device includes each module included, each sub-module included in each module, and each unit, which can be processed by the processor in the electronic device Realize;
  • the processor can be a central processing unit (Central Processing Unit, CPU), a microprocessor (Micro Processing Unit, MPU), a digital signal processor (Digital Signal Processor) Signal Processor, DSP) or Field Programmable Gate Array (Field Programmable Gate Array, FPGA), etc.
  • CPU Central Processing Unit
  • MPU Micro Processing Unit
  • DSP Digital Signal Processor
  • FPGA Field Programmable Gate Array
  • FIG. 6 is a schematic diagram of the composition and structure of a task processing device provided by an embodiment of the present disclosure.
  • the device 600 includes an acquisition module 610, a first conversion module 620, and a second conversion module 630, wherein:
  • the acquiring module 610 is configured to acquire a first panorama of tasks to be processed; wherein, the first panorama includes at least two operation units and resource units corresponding to each of the operation units;
  • the first conversion module 620 is configured to convert the first panorama into an intermediate file, and the attributes of each operation unit in the intermediate file include an input relationship and/or an output relationship with each operation unit. Attributes of the resource unit;
  • the second conversion module 630 is configured to convert the intermediate file into a second panorama; wherein, the program code corresponding to the second panorama is used for calling by a backend task scheduling tool.
  • the first panorama includes attributes of the at least two operation units and attributes of at least one resource unit corresponding to each operation unit; the first conversion module 620, It is further configured to convert the first panorama into an intermediate file based on the attributes of the at least two operation units and the attributes of at least one resource unit corresponding to each of the operation units.
  • the acquisition module 610 includes a determination submodule and a construction submodule, wherein: the determination submodule is configured to determine a plurality of operation units and a plurality of resource units for realizing the task to be processed; Each of the operation units is used to implement the training function, evaluation function or reasoning function in the deep learning model; each of the resource units includes the data input and/or output by the corresponding operation unit during the execution of the processing operation; the The construction submodule is configured to construct a first panorama of the task to be processed in the canvas of the model training platform based on the operation unit of the task to be processed and the plurality of resource units.
  • the first converting module 620 includes a first determining submodule, a combining submodule and a storing submodule, wherein: the first determining submodule is configured to determine the at least two operation units The connection relationship between; the merging submodule is configured to have an input with each of the operation units based on the attributes of the at least two operation units and the attributes of at least one resource unit corresponding to each of the operation units The attribute of the resource unit of the relationship or the output relationship is merged into the attribute of the corresponding operation unit; the storage submodule is configured to save the connection relationship between the at least two operation units and the attribute of the merged operation unit , to get the intermediate file.
  • the storage submodule includes a merging unit and a storage unit, wherein: the merging unit is configured to merge the connection relationship between the at least two operation units into the attributes of the corresponding operation unit; The storage unit is configured to store attributes of the merged at least two operation units to obtain the intermediate file.
  • the first determination submodule is further configured to determine the first operation unit and There is a connection relationship between the second operation units.
  • the second converting module 630 includes a second determining submodule and a generating submodule, wherein: the second determining submodule is configured to determine the intermediate result graph corresponding to the intermediate file; The generation submodule is configured to generate the said intermediate result graph based on the at least two operation units and the preset workflow template corresponding to each operation unit in response to the situation that the intermediate result graph is a directed acyclic graph.
  • the second panorama wherein, the preset workflow template corresponding to each operation unit is set by the front end according to the target task.
  • the second determination submodule includes an extraction unit and a connection unit, wherein: the extraction unit is configured to extract each operation unit in the intermediate file and the connection between each operation unit relationship; the connection unit is configured to connect each operation unit in the intermediate file according to the connection relationship to obtain the intermediate result graph.
  • the generating submodule includes a sorting unit, an obtaining unit, a filling unit, and a generating unit, wherein: the sorting unit is configured to perform topological sorting on all operation units in the intermediate file to obtain Linearly arrange results; the acquisition unit is configured to acquire key information of each of the operation units extracted from the intermediate result map; wherein the key information is configured to fill in necessary fields in the second panorama.
  • the filling unit is configured to sequentially fill the necessary fields in the preset workflow template of the corresponding operation unit based on the key information; the generation unit is configured to arrange the results according to the linear arrangement, based on each operation The preset workflow template corresponding to the unit generates the second panorama.
  • the second conversion module further includes an output submodule configured to output a prompt message indicating an error in creating an intermediate result graph in response to a condition that the intermediate result graph is not a directed acyclic graph.
  • the description of the above device embodiment is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment.
  • the description of the method embodiments of the present disclosure please refer to the description of the method embodiments of the present disclosure for understanding.
  • the above task processing method is implemented in the form of software function modules and sold or used as an independent product, it can also be stored in a computer-readable storage medium.
  • the essence of the technical solutions of the embodiments of the present disclosure or the part that contributes to the related technologies can be embodied in the form of software products, the computer software products are stored in a storage medium, and include several instructions to make An electronic device (which may be a smart phone with a camera, a tablet computer, etc.) executes all or part of the methods described in various embodiments of the present disclosure.
  • the aforementioned storage medium includes: various media that can store program codes such as U disk, mobile hard disk, read-only memory (Read Only Memory, ROM), magnetic disk or optical disk.
  • program codes such as U disk, mobile hard disk, read-only memory (Read Only Memory, ROM), magnetic disk or optical disk.
  • an embodiment of the present disclosure provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps in any one of the task processing methods in the above-mentioned embodiments are implemented, wherein the The storage medium may be a volatile or nonvolatile computer-readable storage medium.
  • a chip is also provided, the chip includes a programmable logic circuit and/or program instructions, and when the chip is running, it is configured to implement the task processing described in any of the above embodiments steps in the method.
  • a computer program product is also provided, and when the computer program product is executed by the processor of the electronic device, it is configured to implement the steps in any one of the task processing methods in the above embodiments .
  • An embodiment of the present disclosure further provides a computer program product, the computer program product carries a program code, and instructions included in the program code can be used to execute the steps in any one of the task processing methods in the above method embodiments.
  • the above-mentioned computer program product may be specifically implemented by means of hardware, software or a combination thereof.
  • the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK) etc. Wait.
  • An embodiment of the present disclosure also provides a computer program, including computer readable codes.
  • the processor in the electronic device executes any one of the above method embodiments.
  • the task processing method is not limited to:
  • FIG. 7 is a schematic diagram of hardware entities of an electronic device provided by an embodiment of the present disclosure.
  • the electronic device 700 includes a memory 710 and a processor 720, and the memory 710 stores a A computer program, the processor 720 implements the steps in any one of the task processing methods in the embodiments of the present disclosure when executing the program.
  • the memory 710 is configured to store instructions and applications executable by the processor 720, and can also cache data to be processed or processed by the processor 720 and various modules in the electronic device (for example, image data, audio data, voice communication data and video data).
  • Communication data which can be realized by flash memory (FLASH) or random access memory (Random Access Memory, RAM).
  • the processor 720 executes the program, the steps of any one of the above-mentioned task processing methods are realized.
  • the processor 720 generally controls the overall operation of the electronic device 700 .
  • the above-mentioned processor can be an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a digital signal processor (Digital Signal Processor, DSP), a digital signal processing device (Digital Signal Processing Device, DSPD), a programmable logic device (Programmable Logic At least one of Device, PLD), Field Programmable Gate Array (Field Programmable Gate Array, FPGA), Central Processing Unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor. It can be understood that the electronic device that realizes the above processor function may also be other, which is not specifically limited in this embodiment of the present disclosure.
  • the above-mentioned computer storage medium/memory can be read-only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), Electrically Erasable Programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), Magnetic Random Access Memory (Ferromagnetic Random Access Memory, FRAM), Flash Memory (Flash Memory), Magnetic Surface Memory, CD-ROM, or CD-ROM (Compact Disc Read-Only Memory, CD-ROM) and other memories; it can also be various electronic devices including one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants Wait.
  • the disclosed devices and methods may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the units is only a logical function division.
  • the coupling, or direct coupling, or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be electrical, mechanical or other forms of.
  • the units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed to multiple network units; Part or all of the units can be selected according to actual needs to achieve the purpose of the solutions of the embodiments of the present disclosure.
  • each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may be used as a single unit, or two or more units may be integrated into one unit; the above-mentioned integration
  • the unit can be realized in the form of hardware or in the form of hardware plus software functional unit.
  • the above-mentioned integrated units of the present disclosure are realized in the form of software function modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.
  • the aforementioned storage medium includes various media capable of storing program codes such as removable storage devices, ROMs, magnetic disks or optical disks.
  • a first panorama of the task to be processed is obtained, and the first panorama includes at least two operation units and resource units corresponding to each of the operation units; then, the first The panorama is converted into an intermediate file, and the attributes of each operation unit in the intermediate file include the attributes of resource units that have an input relationship or an output relationship with each of the operation units; finally, the intermediate file is converted into a second panorama ;
  • the program code corresponding to the second panorama is used for calling by the task scheduling tool at the back end; thus, by defining an intermediate file that can connect the first panorama and meet the needs of conversion into other pictures, the first panorama can be opened
  • the conversion to the second panorama can realize the multi-model serial training process of complex scenes. Compared with the existing technology that can only use a single algorithm module, it is more convenient and faster to build a full-chain algorithm solution.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Human Computer Interaction (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Health & Medical Sciences (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Studio Devices (AREA)

Abstract

Sont divulgués dans des modes de réalisation de la présente divulgation un procédé et un appareil de traitement de tâche, un dispositif, un support d'enregistrement, un programme informatique et un produit programme. Le procédé comprend les étapes consistant à : acquérir un premier panorama d'une tâche à traiter, le premier panorama comprenant au moins deux unités de fonctionnement et des unités de ressources correspondant à chacune des unités de fonctionnement ; convertir le premier panorama en un fichier intermédiaire, un attribut de chaque unité de fonctionnement dans le fichier intermédiaire comprenant un attribut d'une unité de ressource, qui a une relation d'entrée et/ou une relation de sortie avec chacune des unités de fonctionnement ; et convertir le fichier intermédiaire en un second panorama, un code de programme correspondant au second panorama étant utilisé pour être appelé par un outil de planification de tâche au niveau d'une extrémité arrière.
PCT/CN2021/124996 2021-05-25 2021-10-20 Procédé et appareil de traitement de tâche, dispositif, support d'enregistrement, programme informatique et produit programme WO2022247112A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110570294.1A CN113342489A (zh) 2021-05-25 2021-05-25 任务处理方法及装置、电子设备和存储介质
CN202110570294.1 2021-05-25

Publications (1)

Publication Number Publication Date
WO2022247112A1 true WO2022247112A1 (fr) 2022-12-01

Family

ID=77471254

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/124996 WO2022247112A1 (fr) 2021-05-25 2021-10-20 Procédé et appareil de traitement de tâche, dispositif, support d'enregistrement, programme informatique et produit programme

Country Status (2)

Country Link
CN (1) CN113342489A (fr)
WO (1) WO2022247112A1 (fr)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113342489A (zh) * 2021-05-25 2021-09-03 上海商汤智能科技有限公司 任务处理方法及装置、电子设备和存储介质
CN114579183B (zh) * 2022-04-29 2022-10-18 之江实验室 一种用于分布式计算的作业分解处理方法
US11907693B2 (en) 2022-04-29 2024-02-20 Zhejiang Lab Job decomposition processing method for distributed computing

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108073582A (zh) * 2016-11-08 2018-05-25 中移(苏州)软件技术有限公司 一种计算框架选择方法和装置
CN110058985A (zh) * 2019-04-09 2019-07-26 美林数据技术股份有限公司 一种拖拽式构建企业级ai应用的***及方法
US20200057675A1 (en) * 2018-08-16 2020-02-20 EMC IP Holding Company LLC Workflow optimization
CN111240662A (zh) * 2020-01-16 2020-06-05 同方知网(北京)技术有限公司 一种基于任务可视化拖拽的spark机器学习***及学习方法
CN111310936A (zh) * 2020-04-15 2020-06-19 光际科技(上海)有限公司 机器学***台、装置、设备及存储介质
CN111435352A (zh) * 2019-01-11 2020-07-21 北京京东尚科信息技术有限公司 一种分布式实时计算方法、装置、***及其存储介质
CN111488211A (zh) * 2020-04-09 2020-08-04 北京嘀嘀无限科技发展有限公司 基于深度学习框架的任务处理方法、装置、设备及介质
CN111639859A (zh) * 2020-06-01 2020-09-08 腾讯科技（深圳）有限公司 人工智能ai解决方案的模板生成方法和装置及存储介质
CN113342489A (zh) * 2021-05-25 2021-09-03 上海商汤智能科技有限公司 任务处理方法及装置、电子设备和存储介质

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108073582A (zh) * 2016-11-08 2018-05-25 中移(苏州)软件技术有限公司 一种计算框架选择方法和装置
US20200057675A1 (en) * 2018-08-16 2020-02-20 EMC IP Holding Company LLC Workflow optimization
CN111435352A (zh) * 2019-01-11 2020-07-21 北京京东尚科信息技术有限公司 一种分布式实时计算方法、装置、***及其存储介质
CN110058985A (zh) * 2019-04-09 2019-07-26 美林数据技术股份有限公司 一种拖拽式构建企业级ai应用的***及方法
CN111240662A (zh) * 2020-01-16 2020-06-05 同方知网(北京)技术有限公司 一种基于任务可视化拖拽的spark机器学习***及学习方法
CN111488211A (zh) * 2020-04-09 2020-08-04 北京嘀嘀无限科技发展有限公司 基于深度学习框架的任务处理方法、装置、设备及介质
CN111310936A (zh) * 2020-04-15 2020-06-19 光际科技(上海)有限公司 机器学***台、装置、设备及存储介质
CN111639859A (zh) * 2020-06-01 2020-09-08 腾讯科技（深圳）有限公司 人工智能ai解决方案的模板生成方法和装置及存储介质
CN113342489A (zh) * 2021-05-25 2021-09-03 上海商汤智能科技有限公司 任务处理方法及装置、电子设备和存储介质

Also Published As

Publication number Publication date
CN113342489A (zh) 2021-09-03

Similar Documents

Publication Publication Date Title
Brownlee Deep learning for computer vision: image classification, object detection, and face recognition in python
WO2022247112A1 (fr) Procédé et appareil de traitement de tâche, dispositif, support d'enregistrement, programme informatique et produit programme
Shanmugamani Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras
JP6944548B2 (ja) 自動コード生成
US10235602B1 (en) Machine learning artificial intelligence system for identifying vehicles
Villán Mastering OpenCV 4 with Python: a practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7
CN111260545B (zh) 生成图像的方法和装置
CN113453040B (zh) 短视频的生成方法、装置、相关设备及介质
CN112819052B (zh) 多模态细粒度混合方法、***、设备和存储介质
CN111104962A (zh) 图像的语义分割方法、装置、电子设备及可读存储介质
WO2021129181A1 (fr) Procédé de segmentation de portrait, procédé d'apprentissage de modèle et dispositif électronique
AU2021354030B2 (en) Processing images using self-attention based neural networks
CN109522970B (zh) 图像分类方法、装置及***
CN111126347B (zh) 人眼状态识别方法、装置、终端及可读存储介质
Bayraktar et al. A hybrid image dataset toward bridging the gap between real and simulation environments for robotics: Annotated desktop objects real and synthetic images dataset: ADORESet
Thakkar Beginning machine learning in ios: CoreML framework
CN110489613B (zh) 协同可视数据推荐方法及装置
CN115438225A (zh) 视频文本互检方法及其模型训练方法、装置、设备、介质
CN115145568A (zh) 基于图像识别的代码生成方法以及相关设备
CN111445545A (zh) 一种文本转贴图方法、装置、存储介质及电子设备
CN111768214A (zh) 产品属性的预测方法、***、设备和存储介质
Newnham Machine Learning with Core ML: An iOS developer's guide to implementing machine learning in mobile apps
Guo Hair Segmentation using Deep Learning
CN116383426B (zh) 基于属性的视觉情感识别方法、装置、设备及存储介质
CN111898761B (zh) 服务模型生成方法、图像处理方法、装置和电子设备

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21942676

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21942676

Country of ref document: EP

Kind code of ref document: A1