如何在 Ground Truth 中搭配使用自定义 UI 模板及 AWS 提供的 Lambda 函数?

上次更新时间:2020 年 9 月 3 日

我想在标记作业中使用 Amazon SageMaker Ground Truth 自定义 UI 模板和 AWS Lambda 函数。

解决方法

1.    为标记作业创建一个自定义 UI 模板,如以下示例所示。对于语义分割作业,请将 name 变量设置为 crowd-semantic-segmentation,如以下示例所示。对于边界框作业,请将 name 变量设置为 boundingBox。有关自定义模板的增强 HTML 元素的完整列表,请参阅 Crowd HTML 元素参考

<script src="https://assets.crowd.aws/crowd-html-elements.js"></script>
<crowd-form>
    <crowd-semantic-segmentation name="crowd-semantic-segmentation" src="{{ task.input.taskObject | grant_read_access }}" header= "{{ task.input.header }}" labels="{{ task.input.labels | to_json | escape }}">

        <full-instructions header= "Segmentation Instructions">
            <ol>
                <li>Read the task carefully and inspect the image.</li>
                <li>Read the options and review the examples provided to understand more about the labels.</li>
                <li>Choose the appropriate label that best suits the image.</li>
            </ol>
        </full-instructions>

        <short-instructions>
            <p>Use the tools to label the requested items in the image</p>
        </short-instructions>
    </crowd-semantic-segmentation>
</crowd-form>

2.    为标签创建 JSON 文件。示例:

 {
        "labels": [
            {
                "label": "Chair"
            },
            ...
            {
                "label": "Oven"
            }
        ]

}

3.    为图像创建输入清单文件。示例:

{"source-ref":"s3://awsdoc-example-bucket/input_manifest/apartment-chair.jpg"}
{"source-ref":"s3://awsdoc-example-bucket/input_manifest/apartment-carpet.jpg"}

4.    将 HTML、清单文件和 JSON 文件上传至 Amazon Simple Storage Service (Amazon S3)。示例:

import boto3
import os

bucket = 'awsdoc-example-bucket'
prefix = 'GroundTruthCustomUI'

boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'customUI.html')).upload_file('customUI.html')
boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'input.manifest')).upload_file('input.manifest')
boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'testLabels.json')).upload_file('testLabels.json')

5.    检索 Amazon 资源名称 (ARN),以便对 Lambda 函数进行预处理注释合并。例如,以下是语义分割 ARN:

arn:aws:lambda:eu-west-1:111122223333:function:PRE-SemanticSegmentation
arn:aws:lambda:eu-west-1:111122223333:function:ACS-SemanticSegmentation

6.    使用 AWS 开发工具包(如 boto3)创建标记作业。替换下例中的如下值:

INPUT_MANIFEST_IN_S3
S3_OUTPUT_PATH
IAM_ROLE_ARN
LABELS_JSON_FILE_IN_S3
WORKTEAM_ARN
HTML_TEMPLATE_IN_S3

import boto3

client = boto3.client('sagemaker')
client.create_labeling_job(LabelingJobName='SemanticSeg-CustomUI',
    LabelAttributeName='output-ref',
    InputConfig={
        'DataSource': {
            'S3DataSource': {
                'ManifestS3Uri': 'INPUT_MANIFEST_IN_S3'
            }
        },
        'DataAttributes': {
            'ContentClassifiers' : [
                'FreeOfPersonallyIdentifiableInformation',
            ]
        }
    },
    OutputConfig={
        'S3OutputPath' : 'S3_OUTPUT_PATH' 
    },
    RoleArn='IAM_ROLE_ARN',
    LabelCategoryConfigS3Uri='LABELS_JSON_FILE_IN_S3,
    StoppingConditions={
        'MaxPercentageOfInputDatasetLabeled': 100
    },
    HumanTaskConfig={
        'WorkteamArn': 'WORKTEAM_ARN',
        'UiConfig': {
            'UiTemplateS3Uri' : 'HTML_TEMPLATE_IN_S3'
        },
        'PreHumanTaskLambdaArn' : 'arn:aws:lambda:eu-west-1:111122223333:function:PRE-SemanticSegmentation',
        'TaskKeywords': [
            'SemanticSegmentation',
        ],
        'TaskTitle': 'Semantic Segmentation',
        'TaskDescription': 'Draw around the specified labels using the tools',
        'NumberOfHumanWorkersPerDataObject': 1,
        'TaskTimeLimitInSeconds': 3600,
        'TaskAvailabilityLifetimeInSeconds': 1800,
        'MaxConcurrentTaskCount': 1,
        'AnnotationConsolidationConfig': {
            'AnnotationConsolidationLambdaArn': 'arn:aws:lambda:eu-west-1:111122223333:function:ACS-SemanticSegmentation'
        }
    
    },
    Tags=[
        {
            'Key': 'reason',
            'Value': 'CustomUI'
        }
    ])

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