Trainieren eines Amazon Rekognition Custom Labels-Modells

Im folgenden Beispiel wird ein Modell erstellt. Der Trainingsdatensatz wird aufgeteilt, um den Testdatensatz zu erstellen. Ersetzen Sie Folgendes:

aws rekognition create-project-version \
  --project-arn project_arn \
  --version-name version_name \
  --output-config '{"S3Bucket":"output_bucket", "S3KeyPrefix":"output_folder"}' \
  --profile custom-labels-access

Im folgenden Beispiel wird ein Modell erstellt. Geben Sie die folgenden Befehlszeilenargumente an:

Geben Sie optional die folgenden Befehlszeilenparameter an, um Ihrem Modell ein Tag zuzuweisen:

#Copyright 2023 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-custom-labels-developer-guide/blob/master/LICENSE-SAMPLECODE.)


import argparse
import logging
import json
import boto3

from botocore.exceptions import ClientError

logger = logging.getLogger(__name__)

def train_model(rek_client, project_arn, version_name, output_bucket, output_folder, tag_key, tag_key_value):
    """
    Trains an Amazon Rekognition Custom Labels model.
    :param rek_client: The Amazon Rekognition Custom Labels Boto3 client.
    :param project_arn: The ARN of the project in which you want to train a model.
    :param version_name: A version for the model.
    :param output_bucket: The S3 bucket that hosts training output.
    :param output_folder: The path for the training output within output_bucket
    :param tag_key: The name of a tag to attach to the model. Pass None to exclude
    :param tag_key_value: The value of the tag. Pass None to exclude

    """

    try:
        #Train the model

        status=""
        logger.info("training model version %s for project %s",
            version_name, project_arn)


        output_config = json.loads(
            '{"S3Bucket": "'
            + output_bucket
            + '", "S3KeyPrefix": "'
            + output_folder
            + '" }  '
        )

        tags={}

        if tag_key is not None and tag_key_value is not None:
            tags = json.loads(
                '{"' + tag_key + '":"' + tag_key_value + '"}'
            )


        response=rek_client.create_project_version(
            ProjectArn=project_arn, 
            VersionName=version_name,
            OutputConfig=output_config,
            Tags=tags
        )

        logger.info("Started training: %s", response['ProjectVersionArn'])

        # Wait for the project version training to complete.

        project_version_training_completed_waiter = rek_client.get_waiter('project_version_training_completed')
        project_version_training_completed_waiter.wait(ProjectArn=project_arn,
        VersionNames=[version_name])
    

        # Get the completion status.
        describe_response=rek_client.describe_project_versions(ProjectArn=project_arn,
            VersionNames=[version_name])
        for model in describe_response['ProjectVersionDescriptions']:
            logger.info("Status: %s", model['Status'])
            logger.info("Message: %s", model['StatusMessage'])
            status=model['Status']


        logger.info("finished training")

        return response['ProjectVersionArn'], status
    
    except ClientError as err:
        logger.exception("Couldn't create model: %s", err.response['Error']['Message'] )
        raise

def add_arguments(parser):
    """
    Adds command line arguments to the parser.
    :param parser: The command line parser.
    """

    parser.add_argument(
        "project_arn", help="The ARN of the project in which you want to train a model"
    )

    parser.add_argument(
        "version_name", help="A version name of your choosing."
    )

    parser.add_argument(
        "output_bucket", help="The S3 bucket that receives the training results."
    )

    parser.add_argument(
        "output_folder", help="The folder in the S3 bucket where training results are stored."
    )

    parser.add_argument(
        "--tag_name",  help="The name of a tag to attach to the model", required=False
    )

    parser.add_argument(
        "--tag_value",  help="The value for the tag.", required=False
    )




def main():

    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")

    try:

        # Get command line arguments.
        parser = argparse.ArgumentParser(usage=argparse.SUPPRESS)
        add_arguments(parser)
        args = parser.parse_args()

        print(f"Training model version {args.version_name} for project {args.project_arn}")

        # Train the model.
        session = boto3.Session(profile_name='custom-labels-access')
        rekognition_client = session.client("rekognition")

        model_arn, status=train_model(rekognition_client, 
            args.project_arn,
            args.version_name,
            args.output_bucket,
            args.output_folder,
            args.tag_name,
            args.tag_value)


        print(f"Finished training model: {model_arn}")
        print(f"Status: {status}")


    except ClientError as err:
        logger.exception("Problem training model: %s", err)
        print(f"Problem training model: {err}")
    except Exception as err:
        logger.exception("Problem training model: %s", err)
        print(f"Problem training model: {err}")


if __name__ == "__main__":
    main()    
  

Im folgenden Beispiel wird ein Modell trainiert. Geben Sie die folgenden Befehlszeilenargumente an:

/*
   Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
   SPDX-License-Identifier: Apache-2.0
*/
package com.example.rekognition;

import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
import software.amazon.awssdk.core.waiters.WaiterResponse;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.CreateProjectVersionRequest;
import software.amazon.awssdk.services.rekognition.model.CreateProjectVersionResponse;
import software.amazon.awssdk.services.rekognition.model.DescribeProjectVersionsRequest;
import software.amazon.awssdk.services.rekognition.model.DescribeProjectVersionsResponse;
import software.amazon.awssdk.services.rekognition.model.OutputConfig;
import software.amazon.awssdk.services.rekognition.model.ProjectVersionDescription;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.waiters.RekognitionWaiter;

import java.util.Optional;
import java.util.logging.Level;
import java.util.logging.Logger;

public class TrainModel {

    public static final Logger logger = Logger.getLogger(TrainModel.class.getName());

    public static String trainMyModel(RekognitionClient rekClient, String projectArn, String versionName,
            String outputBucket, String outputFolder) {

        try {

            OutputConfig outputConfig = OutputConfig.builder().s3Bucket(outputBucket).s3KeyPrefix(outputFolder).build();

            logger.log(Level.INFO, "Training Model for project {0}", projectArn);
            CreateProjectVersionRequest createProjectVersionRequest = CreateProjectVersionRequest.builder()
                    .projectArn(projectArn).versionName(versionName).outputConfig(outputConfig).build();

            CreateProjectVersionResponse response = rekClient.createProjectVersion(createProjectVersionRequest);

            logger.log(Level.INFO, "Model ARN: {0}", response.projectVersionArn());
            logger.log(Level.INFO, "Training model...");

            // wait until training completes

            DescribeProjectVersionsRequest describeProjectVersionsRequest = DescribeProjectVersionsRequest.builder()
                    .versionNames(versionName)
                    .projectArn(projectArn)
                    .build();

            RekognitionWaiter waiter = rekClient.waiter();

            WaiterResponse<DescribeProjectVersionsResponse> waiterResponse = waiter
                    .waitUntilProjectVersionTrainingCompleted(describeProjectVersionsRequest);

            Optional<DescribeProjectVersionsResponse> optionalResponse = waiterResponse.matched().response();

            DescribeProjectVersionsResponse describeProjectVersionsResponse = optionalResponse.get();

            for (ProjectVersionDescription projectVersionDescription : describeProjectVersionsResponse
                    .projectVersionDescriptions()) {
                System.out.println("ARN: " + projectVersionDescription.projectVersionArn());
                System.out.println("Status: " + projectVersionDescription.statusAsString());
                System.out.println("Message: " + projectVersionDescription.statusMessage());
            }

            return response.projectVersionArn();

        } catch (RekognitionException e) {
            logger.log(Level.SEVERE, "Could not train model: {0}", e.getMessage());
            throw e;
        }

    }

    public static void main(String args[]) {

        String versionName = null;
        String projectArn = null;
        String projectVersionArn = null;
        String bucket = null;
        String location = null;

        final String USAGE = "\n" + "Usage: " + "<project_name> <version_name> <output_bucket> <output_folder>\n\n" + "Where:\n"
                + "   project_arn - The ARN of the project that you want to use. \n\n"
                + "   version_name - A version name for the model.\n\n"
                + "   output_bucket - The S3 bucket in which to place the training output. \n\n"
                + "   output_folder - The folder within the bucket that the training output is stored in. \n\n";

        if (args.length != 4) {
            System.out.println(USAGE);
            System.exit(1);
        }

        projectArn = args[0];
        versionName = args[1];
        bucket = args[2];
        location = args[3];

        try {

            // Get the Rekognition client.
            RekognitionClient rekClient = RekognitionClient.builder()
            .credentialsProvider(ProfileCredentialsProvider.create("custom-labels-access"))
            .region(Region.US_WEST_2)
            .build();


            // Train model
            projectVersionArn = trainMyModel(rekClient, projectArn, versionName, bucket, location);

            System.out.println(String.format("Created model: %s for Project ARN: %s", projectVersionArn, projectArn));

            rekClient.close();

        } catch (RekognitionException rekError) {
            logger.log(Level.SEVERE, "Rekognition client error: {0}", rekError.getMessage());
            System.exit(1);
        }

    }

}