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

Updating the Status of a Team Labeling Acceptance Task

Function

This API is used to update the sample status by confirming the acceptance scope and whether the labeled data is overwritten before the acceptance of the team labeling task is completed.

URI

PUT /v2/{project_id}/datasets/{dataset_id}/workforce-tasks/{workforce_task_id}/acceptance/status

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 team labeling task.

Table 2 Query parameters

Parameter	Mandatory	Type	Description
locale	Yes	String	Language. The options are as follows: en-us: English (default value) zh-cn: Chinese

Request Parameters

Table 3 Request body parameters

Parameter	Mandatory	Type	Description
action	No	Integer	Acceptance action. The options are as follows: 0: Pass all samples. 1: Reject all samples. 2: Cancel acceptance. 3: View the list of conflicted samples. 4: Pass single-accepted samples and unaccepted samples. 5: Pass only single-accepted samples.
locale	No	String	Language. The options are as follows: en-us: English (default value) zh-cn: Chinese
overwrite_last_result	No	Boolean	Whether to overwrite labeled data. The options are as follows: true: Overwrite labeled data. false: Do not overwrite labeled data. (Default value)

Response Parameters

Status code: 200

Table 4 Response body parameters

Parameter	Type	Description
sample_count	Integer	Total number of accepted samples.
samples	Array of DescribeSampleResp objects	List of accepted samples.

Table 5 DescribeSampleResp

Parameter	Type	Description
check_accept	Boolean	Whether the acceptance is passed, which is used for team labeling. The options are as follows: true: The acceptance is passed. false: The acceptance is not passed.
check_comment	String	Acceptance comment, which is used for team labeling.
check_score	String	Acceptance score, which is used for team labeling.
deletion_reasons	Array of strings	Reason for deleting a sample, which is used for healthcare.
hard_details	Map<String,Object>	Details about difficulties, including description, causes, and suggestions of difficult problems.
labelers	Array of Worker objects	Labeling personnel list of sample assignment. The labelers record the team members to which the sample is allocated for team labeling.
labels	Array of SampleLabel objects	Sample label list.
metadata	SampleMetadata object	Key-value pair of the sample metadata attribute.
review_accept	Boolean	Whether to accept the review, which is used for team labeling. The options are as follows: true: accepted false: rejected
review_comment	String	Review comment, which is used for team labeling.
review_score	String	Review score, which is used for team labeling.
sample_data	Array of strings	Sample data list.
sample_dir	String	Sample path.
sample_id	String	Sample ID.
sample_name	String	Sample name.
sample_size	Long	Sample size or text length, in bytes.
sample_status	String	Sample status. The options are as follows: ALL: labeled NONE: unlabeled UNCHECK: pending acceptance ACCEPTED: accepted REJECTED: rejected UNREVIEWED: pending review REVIEWED: reviewed WORKFORCE_SAMPLED: sampled WORKFORCE_SAMPLED_UNCHECK: sampling unchecked WORKFORCE_SAMPLED_CHECKED: sampling checked WORKFORCE_SAMPLED_ACCEPTED: sampling accepted WORKFORCE_SAMPLED_REJECTED: sampling rejected AUTO_ANNOTATION: to be confirmed
sample_time	Long	Sample time, when OBS is last modified.
sample_type	Integer	Sample type. The options are as follows: 0: image 1: text 2: speech 4: table 6: video 9: custom format
score	String	Comprehensive score, which is used for team labeling.
source	String	Source address of sample data.
sub_sample_url	String	Subsample URL, which is used for healthcare.
worker_id	String	ID of a labeling team member, which is used for team labeling.

Table 6 HardDetail

Parameter	Type	Description
alo_name	String	Alias.
id	Integer	Reason ID.
reason	String	Reason description.
suggestion	String	Handling suggestion.

Table 7 Worker

Parameter	Type	Description
create_time	Long	Creation time.
description	String	Labeling team member description. The value contains 0 to 256 characters and does not support the following special characters: ^!<>=&"'
email	String	Email address of a labeling team member.
role	Integer	Role. The options are as follows: 0: labeling personnel 1: reviewer 2: team administrator 3: dataset owner
status	Integer	Current login status of a labeling team member. The options are as follows: 0: The invitation email has not been sent. 1: The invitation email has been sent but the user has not logged in. 2: The user has logged in. 3: The labeling team member has been deleted.
update_time	Long	Update time.
worker_id	String	ID of a labeling team member.
workforce_id	String	ID of a labeling team.

Table 8 SampleLabel

Parameter	Type	Description
annotated_by	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	String	Label ID.
name	String	Label name.
property	SampleLabelProperty object	Attribute key-value pair of the sample label, such as the object shape and shape feature.
score	Float	Confidence.
type	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 9 SampleLabelProperty

Parameter	Type	Description
@modelarts:content	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	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	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	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	String	ID of the head entity in the triplet relationship label, which is a default attribute dedicated to the triplet relationship label.
@modelarts:hard	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	String	Coefficient of difficulty of each label level, which is a default attribute. The value range is [0,1].
@modelarts:hard_reasons	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	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	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	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	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	String	ID of the tail entity in the triplet relationship label, which is a default attribute dedicated to the triplet relationship label.

Table 10 SampleMetadata

Parameter	Type	Description
@modelarts:hard	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	Double	Coefficient of difficulty of each sample level, which is a default attribute. The value range is [0,1].
@modelarts:hard_reasons	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	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.

Example Requests

All Tasks Are Accepted.

{
  "action" : 0
}

Example Responses

Status code: 200

OK

{ }

Status Codes

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

Error Codes

See Error Codes.

Parent topic: Labeling Task Management