分配训练数据集 (SDK)

Amazon Rekognition Custom Labels 需要一个训练数据集和一个测试数据集来训练模型。

如果使用的是 API，可以使用 DistributeDatasetEntries API 将 20% 的训练数据集分配到一个空的测试数据集中。如果只有一个清单文件可用，则分配训练数据集会很有用。使用单个清单文件创建训练数据集。然后，创建一个空的测试数据集并使用 DistributeDatasetEntries 填充测试数据集。

注意

如果使用的是 Amazon Rekognition Custom Labels 控制台并从单数据集项目开始，Amazon Rekognition Custom Labels 将在训练期间拆分（分配）训练数据集以创建测试数据集。20% 的训练数据集条目将移至测试数据集。

分配训练数据集 (SDK)

安装并配置 AWS CLI 和 AWS SDK（如果尚未如此）。有关更多信息，请参阅步骤 4：设置 AWS CLI 以及 AWS SDKs。
创建项目。有关更多信息，请参阅创建 Amazon Rekognition Custom Labels 项目 (SDK)。
创建训练数据集。有关数据集的信息，请参阅创建训练和测试数据集。
创建空测试数据集。

使用以下示例代码将 20% 的训练数据集条目分配到测试数据集。可以通过调用 DescribeProjects 获取项目数据集的 Amazon 资源名称 (ARN)。有关示例代码，请参阅描述项目 (SDK)。

AWS CLI

将 training_dataset-arn 和 test_dataset_arn 的值更改为要使用的数据集的 ARN。


aws rekognition distribute-dataset-entries --datasets ['{"Arn": "training_dataset_arn"}, {"Arn": "test_dataset_arn"}'] \
  --profile custom-labels-access

Python

使用以下代码。提供以下命令行参数：

training_dataset_arn：从中分配条目的训练数据集的 ARN。
test_dataset_arn：将条目分配到的测试数据集的 ARN。


# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0

import argparse
import logging
import time
import json
import boto3

from botocore.exceptions import ClientError

logger = logging.getLogger(__name__)


def check_dataset_status(rek_client, dataset_arn):
    """
    Checks the current status of a dataset.
    :param rek_client: The Amazon Rekognition Custom Labels Boto3 client.
    :param dataset_arn: The dataset that you want to check.
    :return: The dataset status and status message.
    """
    finished = False
    status = ""
    status_message = ""

    while finished is False:

        dataset = rek_client.describe_dataset(DatasetArn=dataset_arn)

        status = dataset['DatasetDescription']['Status']
        status_message = dataset['DatasetDescription']['StatusMessage']

        if status == "UPDATE_IN_PROGRESS":

            logger.info("Distributing dataset: %s ", dataset_arn)
            time.sleep(5)
            continue

        if status == "UPDATE_COMPLETE":
            logger.info(
                "Dataset distribution complete: %s : %s : %s",
                    status, status_message, dataset_arn)
            finished = True
            continue

        if status == "UPDATE_FAILED":
            logger.exception(
                "Dataset distribution failed: %s : %s : %s",
                    status, status_message, dataset_arn)
            finished = True
            break

        logger.exception(
            "Failed. Unexpected state for dataset distribution: %s : %s : %s",
            status, status_message, dataset_arn)
        finished = True
        status_message = "An unexpected error occurred while distributing the dataset"
        break

    return status, status_message


def distribute_dataset_entries(rek_client, training_dataset_arn, test_dataset_arn):
    """
    Distributes 20% of the supplied training dataset into the supplied test dataset.
    :param rek_client: The Amazon Rekognition Custom Labels Boto3 client.
    :param training_dataset_arn: The ARN of the training dataset that you distribute entries from.
    :param test_dataset_arn: The ARN of the test dataset that you distribute entries to.
    """

    try:
        # List dataset labels.
        logger.info("Distributing training dataset entries (%s) into test dataset (%s).",
            training_dataset_arn,test_dataset_arn)
                    

        datasets = json.loads(
            '[{"Arn" : "' + str(training_dataset_arn) + '"},{"Arn" : "' + str(test_dataset_arn) + '"}]')

        rek_client.distribute_dataset_entries(
            Datasets=datasets
        )

        training_dataset_status, training_dataset_status_message = check_dataset_status(
            rek_client, training_dataset_arn)
        test_dataset_status, test_dataset_status_message = check_dataset_status(
            rek_client, test_dataset_arn)

        if training_dataset_status == 'UPDATE_COMPLETE' and test_dataset_status == "UPDATE_COMPLETE":
            print("Distribution complete")
        else:
            print("Distribution failed:")
            print(
                f"\ttraining dataset: {training_dataset_status} : {training_dataset_status_message}")
            print(
                f"\ttest dataset: {test_dataset_status} : {test_dataset_status_message}")

    except ClientError as err:
        logger.exception(
            "Couldn't distribute dataset: %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(
        "training_dataset_arn", help="The ARN of the training dataset that you want to distribute from."
    )

    parser.add_argument(
        "test_dataset_arn", help="The ARN of the test dataset that you want to distribute to."
    )


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"Distributing training dataset entries ({args.training_dataset_arn}) "\
            f"into test dataset ({args.test_dataset_arn}).")

        # Distribute the datasets.

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

        distribute_dataset_entries(rekognition_client,
                                   args.training_dataset_arn,
                                   args.test_dataset_arn)

        print("Finished distributing datasets.")

    except ClientError as err:
        logger.exception("Problem distributing datasets: %s", err)
        print(f"Problem listing dataset labels: {err}")
    except Exception as err:
        logger.exception("Problem distributing datasets: %s", err)
        print(f"Problem distributing datasets: {err}")


if __name__ == "__main__":
    main()

Java V2

使用以下代码。提供以下命令行参数：

training_dataset_arn：从中分配条目的训练数据集的 ARN。
test_dataset_arn：将条目分配到的测试数据集的 ARN。


/*
   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.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.DatasetDescription;
import software.amazon.awssdk.services.rekognition.model.DatasetStatus;
import software.amazon.awssdk.services.rekognition.model.DescribeDatasetRequest;
import software.amazon.awssdk.services.rekognition.model.DescribeDatasetResponse;
import software.amazon.awssdk.services.rekognition.model.DistributeDataset;
import software.amazon.awssdk.services.rekognition.model.DistributeDatasetEntriesRequest;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;

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

public class DistributeDatasetEntries {

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

    public static DatasetStatus checkDatasetStatus(RekognitionClient rekClient, String datasetArn)
            throws Exception, RekognitionException {

        boolean distributed = false;
        DatasetStatus status = null;

        // Wait until distribution completes

        do {

            DescribeDatasetRequest describeDatasetRequest = DescribeDatasetRequest.builder().datasetArn(datasetArn)
                    .build();
            DescribeDatasetResponse describeDatasetResponse = rekClient.describeDataset(describeDatasetRequest);

            DatasetDescription datasetDescription = describeDatasetResponse.datasetDescription();

            status = datasetDescription.status();

            logger.log(Level.INFO, " dataset ARN: {0} ", datasetArn);

            switch (status) {

            case UPDATE_COMPLETE:
                logger.log(Level.INFO, "Dataset updated");
                distributed = true;
                break;

            case UPDATE_IN_PROGRESS:
                Thread.sleep(5000);
                break;

            case UPDATE_FAILED:
                String error = "Dataset distribution failed: " + datasetDescription.statusAsString() + " "
                        + datasetDescription.statusMessage() + " " + datasetArn;
                logger.log(Level.SEVERE, error);
                break;

            default:
                String unexpectedError = "Unexpected distribution state: " + datasetDescription.statusAsString() + " "
                        + datasetDescription.statusMessage() + " " + datasetArn;
                logger.log(Level.SEVERE, unexpectedError);

            }

        } while (distributed == false);

        return status;

    }

    public static void distributeMyDatasetEntries(RekognitionClient rekClient, String trainingDatasetArn,
            String testDatasetArn) throws Exception, RekognitionException {

        try {

            logger.log(Level.INFO, "Distributing {0} dataset to {1} ",
                    new Object[] { trainingDatasetArn, testDatasetArn });

            DistributeDataset distributeTrainingDataset = DistributeDataset.builder().arn(trainingDatasetArn).build();

            DistributeDataset distributeTestDataset = DistributeDataset.builder().arn(testDatasetArn).build();

            ArrayList<DistributeDataset> datasets = new ArrayList();

            datasets.add(distributeTrainingDataset);
            datasets.add(distributeTestDataset);

            DistributeDatasetEntriesRequest distributeDatasetEntriesRequest = DistributeDatasetEntriesRequest.builder()
                    .datasets(datasets).build();

            rekClient.distributeDatasetEntries(distributeDatasetEntriesRequest);

            DatasetStatus trainingStatus = checkDatasetStatus(rekClient, trainingDatasetArn);
            DatasetStatus testStatus = checkDatasetStatus(rekClient, testDatasetArn);

            if (trainingStatus == DatasetStatus.UPDATE_COMPLETE && testStatus == DatasetStatus.UPDATE_COMPLETE) {
                logger.log(Level.INFO, "Successfully distributed dataset: {0}", trainingDatasetArn);

            } else {

                throw new Exception("Failed to distribute dataset: " + trainingDatasetArn);
            }

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

    }

    public static void main(String[] args) {

        String trainingDatasetArn = null;
        String testDatasetArn = null;

        final String USAGE = "\n" + "Usage: " + "<training_dataset_arn> <test_dataset_arn>\n\n" + "Where:\n"
                + "   training_dataset_arn - the ARN of the dataset that you want to distribute from.\n\n"
                + "   test_dataset_arn - the ARN of the dataset that you want to distribute to.\n\n";

        if (args.length != 2) {
            System.out.println(USAGE);
            System.exit(1);
        }

        trainingDatasetArn = args[0];
        testDatasetArn = args[1];

        try {

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

            // Distribute the dataset
            distributeMyDatasetEntries(rekClient, trainingDatasetArn, testDatasetArn);

            System.out.println("Datasets distributed.");

            rekClient.close();

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

    }

}

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文档惯例

列出数据集条目 (SDK)

删除数据集