How can I use a custom UI template with AWS-provided Lambda functions in Ground Truth?

Last updated: 2020-09-03

I want to use an Amazon SageMaker Ground Truth custom UI template and AWS Lambda functions for a labeling job.

Resolution

1.    Create a custom UI template for the labeling job, as shown in the following example. For semantic segmentation jobs, set the name variable to crowd-semantic-segmentation, as shown in the following example. For bounding box jobs, set the name variable to boundingBox. For a full list of enhanced HTML elements for custom templates, see Crowd HTML elements reference.

<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.    Create a JSON file for the labels. Example:

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

}

3.    Create an input manifest file for the images. Example:

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

4.    Upload the HTML, manifest, and JSON files to Amazon Simple Storage Service (Amazon S3). Example:

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.    Retrieve the Amazon Resource Names (ARNs) for the pre-processing and annotation consolidation Lambda functions. For example, here are the semantic segmentation ARNs:

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

6.    Create the labeling job using an AWS SDK, such as boto3. Replace these values in the following example:

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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