doc-exports/docs/modelarts/api-ref/UpdateWorkforceTaskSamples.html

Updating Labels of Team Labeling Samples in Batches

Function

This API is used to update labels of team labeling samples in batches.

URI

PUT /v2/{project_id}/datasets/{dataset_id}/workforce-tasks/{workforce_task_id}/data-annotations/samples

Table 1 Path Parameters

Parameter	Mandatory	Type	Description
dataset_id	Yes	String	Dataset ID.
project_id	Yes	String	Project ID. For details about how to obtain the project ID, see Obtaining a Project ID.
workforce_task_id	Yes	String	ID of a labeling task.

Request Parameters

Table 2 Request body parameters

Parameter	Mandatory	Type	Description
email	No	String	Email address of a labeling team member.
samples	No	Array of SampleLabels objects	Updated sample list.

Table 3 SampleLabels

Parameter	Mandatory	Type	Description
labels	No	Array of SampleLabel objects	Sample label list. If this parameter is left blank, all sample labels are deleted.
metadata	No	SampleMetadata object	Key-value pair of the sample metadata attribute.
sample_id	No	String	Sample ID.
sample_type	No	Integer	Sample type. The options are as follows: 0: image 1: text 2: speech 4: table 6: video 9: custom format
sample_usage	No	String	Sample usage. The options are as follows: TRAIN: training EVAL: evaluation TEST: test INFERENCE: inference
source	No	String	Source address of sample data.
worker_id	No	String	ID of a labeling team member.

Table 4 SampleLabel

Parameter	Mandatory	Type	Description
annotated_by	No	String	Video labeling method, which is used to distinguish whether a video is labeled manually or automatically. The options are as follows: human: manual labeling auto: automatic labeling
id	No	String	Label ID.
name	No	String	Label name.
property	No	SampleLabelProperty object	Attribute key-value pair of the sample label, such as the object shape and shape feature.
score	No	Float	Confidence.
type	No	Integer	Label type. The options are as follows: 0: image classification 1: object detection 100: text classification 101: named entity recognition 102: text triplet relationship 103: text triplet entity 200: speech classification 201: speech content 202: speech paragraph labeling 600: video classification

Table 5 SampleLabelProperty

Parameter	Mandatory	Type	Description
@modelarts:content	No	String	Speech text content, which is a default attribute dedicated to the speech label (including the speech content and speech start and end points).
@modelarts:end_index	No	Integer	End position of the text, which is a default attribute dedicated to the named entity label. The end position does not include the character corresponding to the value of end_index. Examples are as follows.- If the text content is "Barack Hussein Obama II (born August 4, 1961) is an American attorney and politician.", the start_index and end_index values of "Barack Hussein Obama II" are 0 and 23, respectively.- If the text content is "By the end of 2018, the company has more than 100 employees.", the start_index and end_index values of "By the end of 2018" are 0 and 18, respectively.
@modelarts:end_time	No	String	Speech end time, which is a default attribute dedicated to the speech start/end point label, in the format of hh:mm:ss.SSS. (hh indicates hour; mm indicates minute; ss indicates second; and SSS indicates millisecond.)
@modelarts:feature	No	Object	Shape feature, which is a default attribute dedicated to the object detection label, with type of List. The upper left corner of an image is used as the coordinate origin [0,0]. Each coordinate point is represented by [x, y]. x indicates the horizontal coordinate, and y indicates the vertical coordinate (both x and y are greater than or equal to 0). The format of each shape is as follows:- bndbox: consists of two points, for example, 0,10],[50,95. The first point is located at the upper left corner of the rectangle and the second point is located at the lower right corner of the rectangle. That is, the X coordinate of the first point must be smaller than that of the second point, and the Y coordinate of the second point must be smaller than that of the first point.- polygon: consists of multiple points that are connected in sequence to form a polygon, for example, 0,100],[50,95],[10,60],[500,400.- circle: consists of the center point and radius, for example, 100,100],[50.- line: consists of two points, for example, 0,100],[50,95. The first point is the start point, and the second point is the end point.- dashed: consists of two points, for example, 0,100],[50,95. The first point is the start point, and the second point is the end point.- point: consists of one point, for example, 0,100.- polyline: consists of multiple points, for example, 0,100],[50,95],[10,60],[500,400.
@modelarts:from	No	String	ID of the head entity in the triplet relationship label, which is a default attribute dedicated to the triplet relationship label.
@modelarts:hard	No	String	Sample labeled as a hard sample or not, which is a default attribute. Options: 0/false: not a hard example 1/true: hard example
@modelarts:hard_coefficient	No	String	Coefficient of difficulty of each label level, which is a default attribute. The value range is [0,1].
@modelarts:hard_reasons	No	String	Reasons that the sample is a hard sample, which is a default attribute. Use a hyphen (-) to separate every two hard sample reason IDs, for example, 3-20-21-19. The options are as follows: 0: No target objects are identified. 1: The confidence is low. 2: The clustering result based on the training dataset is inconsistent with the prediction result. 3: The prediction result is greatly different from the data of the same type in the training dataset. 4: The prediction results of multiple consecutive similar images are inconsistent. 5: There is a large offset between the image resolution and the feature distribution of the training dataset. 6: There is a large offset between the aspect ratio of the image and the feature distribution of the training dataset. 7: There is a large offset between the brightness of the image and the feature distribution of the training dataset. 8: There is a large offset between the saturation of the image and the feature distribution of the training dataset. 9: There is a large offset between the color richness of the image and the feature distribution of the training dataset. 10: There is a large offset between the definition of the image and the feature distribution of the training dataset. 11: There is a large offset between the number of frames of the image and the feature distribution of the training dataset. 12: There is a large offset between the standard deviation of area of image frames and the feature distribution of the training dataset. 13: There is a large offset between the aspect ratio of image frames and the feature distribution of the training dataset. 14: There is a large offset between the area portion of image frames and the feature distribution of the training dataset. 15: There is a large offset between the edge of image frames and the feature distribution of the training dataset. 16: There is a large offset between the brightness of image frames and the feature distribution of the training dataset. 17: There is a large offset between the definition of image frames and the feature distribution of the training dataset. 18: There is a large offset between the stack of image frames and the feature distribution of the training dataset. 19: The data enhancement result based on GaussianBlur is inconsistent with the prediction result of the original image. 20: The data enhancement result based on fliplr is inconsistent with the prediction result of the original image. 21: The data enhancement result based on Crop is inconsistent with the prediction result of the original image. 22: The data enhancement result based on flipud is inconsistent with the prediction result of the original image. 23: The data enhancement result based on scale is inconsistent with the prediction result of the original image. 24: The data enhancement result based on translate is inconsistent with the prediction result of the original image. 25: The data enhancement result based on shear is inconsistent with the prediction result of the original image. 26: The data enhancement result based on superpixels is inconsistent with the prediction result of the original image. 27: The data enhancement result based on sharpen is inconsistent with the prediction result of the original image. 28: The data enhancement result based on add is inconsistent with the prediction result of the original image. 29: The data enhancement result based on invert is inconsistent with the prediction result of the original image. 30: The data is predicted to be abnormal.
@modelarts:shape	No	String	Object shape, which is a default attribute dedicated to the object detection label and is left empty by default. The options are as follows: bndbox: rectangle polygon: polygon circle: circle line: straight line dashed: dotted line point: point polyline: polyline
@modelarts:source	No	String	Speech source, which is a default attribute dedicated to the speech start/end point label and can be set to a speaker or narrator.
@modelarts:start_index	No	Integer	Start position of the text, which is a default attribute dedicated to the named entity label. The start value begins from 0, including the character corresponding to the value of start_index.
@modelarts:start_time	No	String	Speech start time, which is a default attribute dedicated to the speech start/end point label, in the format of hh:mm:ss.SSS. (hh indicates hour; mm indicates minute; ss indicates second; and SSS indicates millisecond.)
@modelarts:to	No	String	ID of the tail entity in the triplet relationship label, which is a default attribute dedicated to the triplet relationship label.

Table 6 SampleMetadata

Parameter	Mandatory	Type	Description
@modelarts:hard	No	Double	Whether the sample is labeled as a hard sample, which is a default attribute. The options are as follows: 0: non-hard sample 1: hard sample
@modelarts:hard_coefficient	No	Double	Coefficient of difficulty of each sample level, which is a default attribute. The value range is [0,1].
@modelarts:hard_reasons	No	Array of integers	ID of a hard sample reason, which is a default attribute. The options are as follows: 0: No target objects are identified. 1: The confidence is low. 2: The clustering result based on the training dataset is inconsistent with the prediction result. 3: The prediction result is greatly different from the data of the same type in the training dataset. 4: The prediction results of multiple consecutive similar images are inconsistent. 5: There is a large offset between the image resolution and the feature distribution of the training dataset. 6: There is a large offset between the aspect ratio of the image and the feature distribution of the training dataset. 7: There is a large offset between the brightness of the image and the feature distribution of the training dataset. 8: There is a large offset between the saturation of the image and the feature distribution of the training dataset. 9: There is a large offset between the color richness of the image and the feature distribution of the training dataset. 10: There is a large offset between the definition of the image and the feature distribution of the training dataset. 11: There is a large offset between the number of frames of the image and the feature distribution of the training dataset. 12: There is a large offset between the standard deviation of area of image frames and the feature distribution of the training dataset. 13: There is a large offset between the aspect ratio of image frames and the feature distribution of the training dataset. 14: There is a large offset between the area portion of image frames and the feature distribution of the training dataset. 15: There is a large offset between the edge of image frames and the feature distribution of the training dataset. 16: There is a large offset between the brightness of image frames and the feature distribution of the training dataset. 17: There is a large offset between the definition of image frames and the feature distribution of the training dataset. 18: There is a large offset between the stack of image frames and the feature distribution of the training dataset. 19: The data enhancement result based on GaussianBlur is inconsistent with the prediction result of the original image. 20: The data enhancement result based on fliplr is inconsistent with the prediction result of the original image. 21: The data enhancement result based on Crop is inconsistent with the prediction result of the original image. 22: The data enhancement result based on flipud is inconsistent with the prediction result of the original image. 23: The data enhancement result based on scale is inconsistent with the prediction result of the original image. 24: The data enhancement result based on translate is inconsistent with the prediction result of the original image. 25: The data enhancement result based on shear is inconsistent with the prediction result of the original image. 26: The data enhancement result based on superpixels is inconsistent with the prediction result of the original image. 27: The data enhancement result based on sharpen is inconsistent with the prediction result of the original image. 28: The data enhancement result based on add is inconsistent with the prediction result of the original image. 29: The data enhancement result based on invert is inconsistent with the prediction result of the original image. 30: The data is predicted to be abnormal.
@modelarts:size	No	Array of objects	Image size (width, height, and depth of the image), which is a default attribute, with type of List. In the list, the first number indicates the width (pixels), the second number indicates the height (pixels), and the third number indicates the depth (the depth can be left blank and the default value is 3). For example, [100,200,3] and [100,200] are both valid. Note: This parameter is mandatory only when the sample label list contains the object detection label.

Response Parameters

Status code: 200

Table 7 Response body parameters

Parameter	Type	Description
error_code	String	Error code.
error_msg	String	Error message.
results	Array of BatchResponse objects	Response list for updating sample labels in batches.
success	Boolean	Whether the operation is successful. The options are as follows: true: successful false: failed

Table 8 BatchResponse

Parameter	Type	Description
error_code	String	Error code.
error_msg	String	Error message.
success	Boolean	Check whether the operation is successful. The options are as follows: true: The operation is successful. false: The operation is failed.

Example Requests

Updating Labels of Team Labeling Samples in Batches

{
  "samples" : [ {
    "sample_id" : "0a0939d6d3c48a3d2a2619245943ac21",
    "worker_id" : "8c15ad080d3eabad14037b4eb00d6a6f",
    "labels" : [ {
      "name" : "tulips"
    } ]
  }, {
    "sample_id" : "0e1b5a16a5a577ee53aeb34278a4b3e7",
    "worker_id" : "8c15ad080d3eabad14037b4eb00d6a6f",
    "labels" : [ {
      "name" : "tulips"
    } ]
  } ]
}

Example Responses

Status code: 200

OK

{
  "success" : true
}

Status Codes

Status Code	Description
200	OK
401	Unauthorized
403	Forbidden
404	Not Found

Error Codes

See Error Codes.

Parent topic: Team Labeling Process Management