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Pipcook Interfaces

Index

Enumerations

Classes

Interfaces

Type aliases

Variables

Functions

Object literals

Type aliases

DataflowEntry

DataflowEntry<IN, IN_META, OUT, OUT_META>: (api: DatasetPool<IN, IN_META>, options: Record<string, any>, context: ScriptContext) => Promise<DatasetPool<OUT, OUT_META>>

type of data flow script entry

Type parameters

  • IN: Sample<any>

  • IN_META: DatasetMeta

  • OUT: Sample<any>

  • OUT_META: DatasetMeta

Type declaration

    • (api: DatasetPool<IN, IN_META>, options: Record<string, any>, context: ScriptContext): Promise<DatasetPool<OUT, OUT_META>>

    • Parameters

      • api: DatasetPool<IN, IN_META>
      • options: Record<string, any>
      • context: ScriptContext

      Returns Promise<DatasetPool<OUT, OUT_META>>

Dataset

Dataset: BaseDataset<Sample>

DatasetPool

DatasetPool<T, D>: BaseDatasetPool<Sample, DatasetMeta>

data source api

Type parameters

  • T: Sample

  • D: DatasetMeta

getDatasetMeta

getDatasetMeta: () => Promise<D | undefined>

Type declaration

    • (): Promise<D | undefined>

    • Returns Promise<D | undefined>

Optional predicted

predicted: Dataset<T>

shuffle

shuffle: (seed?: undefined | string) => void

Type declaration

    • (seed?: undefined | string): void

    • Parameters

      • Optional seed: undefined | string

      Returns void

Optional test

test: Dataset<T>

Optional train

train: Dataset<T>

Optional valid

valid: Dataset<T>

transform

  • transform<TARGET_SAMPLE>(transformFun: (sample: T) => Promise<TARGET_SAMPLE>): DatasetPool<TARGET_SAMPLE, D>
  • transform<TARGET_SAMPLE, TARGET_META>(opts: TransformOption<T, D, TARGET_SAMPLE, TARGET_META>): DatasetPool<TARGET_SAMPLE, TARGET_META>

DatasourceEntry

DatasourceEntry<SAMPLE, META>: (options: Record<string, any>, context: ScriptContext) => Promise<DatasetPool<SAMPLE, META>>

type of data source script entry

Type parameters

  • SAMPLE: Sample<any>

  • META: DatasetMeta

Type declaration

    • (options: Record<string, any>, context: ScriptContext): Promise<DatasetPool<SAMPLE, META>>

    • Parameters

      Returns Promise<DatasetPool<SAMPLE, META>>

FrameworkModule

FrameworkModule: any

ModelEntry

ModelEntry<SAMPLE, META>: (api: Runtime<SAMPLE, META>, options: Record<string, any>, context: ScriptContext) => Promise<void>

type of model script entry for train

Type parameters

  • SAMPLE: Sample<any>

  • META: DatasetMeta

Type declaration

    • Parameters

      Returns Promise<void>

PredictEntry

PredictEntry<SAMPLE, META>: (api: Runtime<SAMPLE, META>, options: Record<string, any>, context: ScriptContext) => Promise<PredictResult>

type of model script entry for predict

Type parameters

  • SAMPLE: Sample<any>

  • META: DatasetMeta

Type declaration

PredictResult

PredictResult: Types.ObjectDetection.PredictResult | Types.TextClassification.PredictResult | Types.ImageClassification.PredictResult | any

Sample

Sample: Sample

SinglePredictResult

SinglePredictResult: Array<PredictObject>

category

category: string

id

id: number

score

score: number

Variables

ArrayDatasetImpl

ArrayDatasetImpl: ArrayDatasetImpl

BaseDataset

BaseDataset: any

Category

Category: any

Coco

Coco: "/Users/runner/work/pipcook/pipcook/node_modules/@pipcook/datacook/dist/dataset/types/coco"

Csv

Csv: "/Users/runner/work/pipcook/pipcook/node_modules/@pipcook/datacook/dist/dataset/types/csv"

Dataset

Dataset: any

DatasetMate

DatasetMate: any

DatasetType

DatasetType: DatasetType

ImageClassification

ImageClassification: "/Users/runner/work/pipcook/pipcook/node_modules/@pipcook/datacook/dist/dataset/types/image-classification"

ImageDimension

ImageDimension: any

Info

Info: any

License

License: any

ObjectDetection

ObjectDetection: "/Users/runner/work/pipcook/pipcook/node_modules/@pipcook/datacook/dist/dataset/types/object-detection"

PascalVoc

PascalVoc: "/Users/runner/work/pipcook/pipcook/node_modules/@pipcook/datacook/dist/dataset/types/pascal-voc"

Sample

Sample: any

TableSchema

TableSchema: any

TextClassification

TextClassification: "/Users/runner/work/pipcook/pipcook/node_modules/@pipcook/datacook/dist/dataset/types/text-classification"

Types

Types: "/Users/runner/work/pipcook/pipcook/node_modules/@pipcook/datacook/dist/dataset/types/index"

Const csvDataWithHead

csvDataWithHead: "A,B,C1,2,34,5,67,8,9" = "A,B,C1,2,34,5,67,8,9"

Const csvDataWithoutHead

csvDataWithoutHead: "1,2,34,5,67,8,9" = "1,2,34,5,67,8,9"

Const pascalVocAnnotation

pascalVocAnnotation: Array<Annotation> = [{annotation: {folder: 'images',filename: '0001.jpg',path: 'images/0001.jpg',source: {database: 'database',annotation: 'source.annotation',image: 'source.image',flickrid: '123'},owner: {flickrid: 'owner.flickerid',name: 'owner.name'},size: {width: 234,height: 345,depth: 3},segmented: 0,object: [{// id: 0,name: 'dog',pose: 'Left',truncated: 1,difficult: 0,bndbox: {xmin: 48,ymin: 240,xmax: 195,ymax: 371}},{// id: 1,name: 'person',pose: 'Left',truncated: 1,difficult: 0,bndbox: {xmin: 8,ymin: 12,xmax: 352,ymax: 498}}]}},{annotation: {folder: 'images',filename: '0002.jpg',path: 'images/0002.jpg',source: {database: 'database',annotation: 'source.annotation',image: 'source.image',flickrid: '123'},owner: {flickrid: 'owner.flickerid',name: 'owner.name'},size: {width: 234,height: 345,depth: 3},segmented: 0,object: [{// id: 0,name: 'dog',pose: 'Left',truncated: 1,difficult: 0,bndbox: {xmin: 48,ymin: 240,xmax: 195,ymax: 371}},{// id: 1,name: 'person',pose: 'Left',truncated: 1,difficult: 0,bndbox: {xmin: 8,ymin: 12,xmax: 352,ymax: 498}}]}},{annotation: {folder: 'images',filename: '0003.jpg',path: 'images/0003.jpg',source: {database: 'database',annotation: 'source.annotation',image: 'source.image',flickrid: '123'},owner: {flickrid: 'owner.flickerid',name: 'owner.name'},size: {width: 235,height: 346,depth: 3},segmented: 0,object: [{// id: 2,name: 'dog2',pose: 'Left',truncated: 1,difficult: 0,bndbox: {xmin: 48,ymin: 240,xmax: 195,ymax: 371}},{// id: 3,name: 'person2',pose: 'Left',truncated: 1,difficult: 0,bndbox: {xmin: 8,ymin: 12,xmax: 352,ymax: 498}}]}}]

Functions

isDatasetData

isTransformOption

  • isTransformOption<T, D, TARGET_SAMPLE, TARGET_META>(arg: TransformOption<T, D, TARGET_SAMPLE, TARGET_META> | ((sample: T) => Promise<TARGET_SAMPLE>)): arg is TransformOption<T, D, TARGET_SAMPLE, TARGET_META>

  • Type parameters

    • T: Sample

    • D: DatasetMeta

    • TARGET_SAMPLE: Sample

    • TARGET_META: DatasetMeta

    Parameters

    • arg: TransformOption<T, D, TARGET_SAMPLE, TARGET_META> | ((sample: T) => Promise<TARGET_SAMPLE>)

    Returns arg is TransformOption<T, D, TARGET_SAMPLE, TARGET_META>

Const makeDatasetPoolFromCocoFormat

  • makeDatasetPoolFromCocoFormat(options: Options): Promise<DatasetPool<Sample<DataCook.Dataset.Types.Coco.Image, DataCook.Dataset.Types.Coco.Label>, DatasetMeta>>

Const makeDatasetPoolFromCsv

  • makeDatasetPoolFromCsv(options: Options): DatasetPool<Sample, DatasetMeta>

Const makeDatasetPoolFromPascalVoc

  • makeDatasetPoolFromPascalVoc(options: Options): Promise<DatasetPool<Sample, DatasetMeta>>

Const makeImageClassificationDataset

Const makeImageClassificationDatasetFromList

  • makeImageClassificationDatasetFromList(opts: Options): DatasetPool

Const makeObjectDetectionDataset

  • makeObjectDetectionDataset(datasetData: DatasetData<Sample>, meta: DatasetMeta): DatasetPool

Const makeObjectDetectionDatasetFromCoco

  • makeObjectDetectionDatasetFromCoco(options: CocoDataset.Options): Promise<DatasetPool>

Const makeObjectDetectionDatasetFromPascalVoc

  • makeObjectDetectionDatasetFromPascalVoc(options: Options): Promise<DatasetPool>

Const makeTextClassificationDataset

Const makeTextClassificationDatasetFromList

  • makeTextClassificationDatasetFromList(opts: Options): DatasetPool

toSamples

  • toSamples(parsedData: ParseResult<Record<string, string>>, labelFields?: Array<string>): Array<Sample>

Object literals

Const annotationObj

annotationObj: object

annotations

annotations: { bbox: [number, number, number, number]; category_id: number; id: number; image_id: number; iscrowd: 0; segmentation: never[] }[] = [{image_id: 1,id: 1,segmentation: [],iscrowd: 0,bbox: [36,36,210,250],category_id: 1},{image_id: 1,id: 2,segmentation: [],iscrowd: 0,bbox: [270,36,210,250],category_id: 1},{image_id: 2,id: 3,segmentation: [],iscrowd: 0,bbox: [270,36,210,250],category_id: 1},{image_id: 2,id: 4,segmentation: [],iscrowd: 0,bbox: [170,36,110,150],category_id: 2},{image_id: 3,id: 5,segmentation: [],iscrowd: 0,bbox: [150,136,110,50],category_id: 1}]

categories

categories: { id: number; name: string; supercategory: string }[] = [{supercategory: 'abovePicture',id: 1,name: 'abovePicture'},{supercategory: 'button',id: 2,name: 'button'}]

images

images: { file_name: string; height: number; id: number; url: string; width: number }[] = [{file_name: 'f984d880-1cb6-11ea-a3c0-69b27346a20f-screenshot.png',width: 750,url: 'img/f984d880-1cb6-11ea-a3c0-69b27346a20f-screenshot.png',id: 1,height: 286},{file_name: 'fb6a8870-1cb6-11ea-a3c0-69b27346a20f-screenshot.png',width: 750,url: 'img/fb6a8870-1cb6-11ea-a3c0-69b27346a20f-screenshot.png',id: 2,height: 363},{file_name: 'fd5abfb0-1cb6-11ea-a3c0-69b27346a20f-screenshot.png',width: 750,url: 'img/fd5abfb0-1cb6-11ea-a3c0-69b27346a20f-screenshot.png',id: 3,height: 286}]

Const cocoAnnotation

cocoAnnotation: object

annotations

annotations: { bbox: [number, number, number, number]; category_id: number; id: number; image_id: number; iscrowd: 0; segmentation: never[] }[] = [{image_id: 1,id: 1,segmentation: [],iscrowd: 0,bbox: [36,36,210,250],category_id: 1},{image_id: 1,id: 2,segmentation: [],iscrowd: 0,bbox: [270,36,210,250],category_id: 1},{image_id: 2,id: 3,segmentation: [],iscrowd: 0,bbox: [270,36,210,250],category_id: 1},{image_id: 2,id: 4,segmentation: [],iscrowd: 0,bbox: [170,36,110,150],category_id: 2},{image_id: 3,id: 5,segmentation: [],iscrowd: 0,bbox: [150,136,110,50],category_id: 1}]

categories

categories: { id: number; name: string; supercategory: string }[] = [{supercategory: 'abovePicture',id: 1,name: 'abovePicture'},{supercategory: 'button',id: 2,name: 'button'}]

images

images: { file_name: string; height: number; id: number; url: string; width: number }[] = [{file_name: 'f984d880-1cb6-11ea-a3c0-69b27346a20f-screenshot.png',width: 750,url: 'img/f984d880-1cb6-11ea-a3c0-69b27346a20f-screenshot.png',id: 1,height: 286},{file_name: 'fb6a8870-1cb6-11ea-a3c0-69b27346a20f-screenshot.png',width: 750,url: 'img/fb6a8870-1cb6-11ea-a3c0-69b27346a20f-screenshot.png',id: 2,height: 363},{file_name: 'fd5abfb0-1cb6-11ea-a3c0-69b27346a20f-screenshot.png',width: 750,url: 'img/fd5abfb0-1cb6-11ea-a3c0-69b27346a20f-screenshot.png',id: 3,height: 286}]

Const sample1

sample1: object

data

data: object = annotationObj.images[0]

A

A: string = "1"

B

B: string = "2"

annotation

annotation: object

object

object: any[] = [{ ...(pascalVocAnnotation[1].annotation.object as Array<any>)[0], name: 'dog' }, { ...(pascalVocAnnotation[1].annotation.object as Array<any>)[1], name: 'person' }]

label

label: object = [{ ...(pascalVocAnnotation[1].annotation.object as Array<any>)[0], name: 'dog' }, { ...(pascalVocAnnotation[1].annotation.object as Array<any>)[1], name: 'person' }]

C

C: string = "3"

Const sample2

sample2: object

data

data: object = annotationObj.images[1]

A

A: string = "4"

B

B: string = "5"

annotation

annotation: object

object

object: any[] = [{ ...(pascalVocAnnotation[1].annotation.object as Array<any>)[0], name: 'dog' }, { ...(pascalVocAnnotation[1].annotation.object as Array<any>)[1], name: 'person' }]

label

label: object = [ { ...(pascalVocAnnotation[1].annotation.object as Array<any>)[0], name: 'dog' }, { ...(pascalVocAnnotation[1].annotation.object as Array<any>)[1], name: 'person' } ]

C

C: string = "6"

Const sample3

sample3: object

data

data: object = annotationObj.images[2]

A

A: string = "7"

B

B: string = "8"

annotation

annotation: object

object

object: any[] = [{ ...(pascalVocAnnotation[2].annotation.object as Array<any>)[0], name: 'dog2' }, { ...(pascalVocAnnotation[2].annotation.object as Array<any>)[1], name: 'person2' }]

label

label: object = [ { ...(pascalVocAnnotation[2].annotation.object as Array<any>)[0], name: 'dog2' }, { ...(pascalVocAnnotation[2].annotation.object as Array<any>)[1], name: 'person2' } ]

C

C: string = "9"

Const sampleNoHead1

sampleNoHead1: object

data

data: object

0

0: string = "1"

1

1: string = "2"

label

label: object

2

2: string = "3"

Const sampleNoHead2

sampleNoHead2: object

data

data: object

0

0: string = "4"

1

1: string = "5"

label

label: object

2

2: string = "6"

Const sampleNoHead3

sampleNoHead3: object

data

data: object

0

0: string = "7"

1

1: string = "8"

label

label: object

2

2: string = "9"

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