Struct google_api_proto::google::cloud::automl::v1::InputConfig

pub struct InputConfig {
    pub params: BTreeMap<String, String>,
    pub source: Option<Source>,
}

Input configuration for [AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData] action.

The format of input depends on dataset_metadata the Dataset into which the import is happening has. As input source the [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] is expected, unless specified otherwise. Additionally any input .CSV file by itself must be 100MB or smaller, unless specified otherwise. If an “example” file (that is, image, video etc.) with identical content (even if it had different GCS_FILE_PATH) is mentioned multiple times, then its label, bounding boxes etc. are appended. The same file should be always provided with the same ML_USE and GCS_FILE_PATH, if it is not, then these values are nondeterministically selected from the given ones.

The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are:

AutoML Vision

Classification

See Preparing your training data for more information.

CSV file(s) with each line in format:

 ML_USE,GCS_FILE_PATH,LABEL,LABEL,...

• ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:

  • TRAIN - Rows in this file are used to train the model.
  • TEST - Rows in this file are used to test the model during training.
  • UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.

• GCS_FILE_PATH - The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO.

• LABEL - A label that identifies the object in the image.

For the MULTICLASS classification type, at most one LABEL is allowed per image. If an image has not yet been labeled, then it should be mentioned just once with no LABEL.

Some sample rows:

 TRAIN,gs://folder/image1.jpg,daisy
 TEST,gs://folder/image2.jpg,dandelion,tulip,rose
 UNASSIGNED,gs://folder/image3.jpg,daisy
 UNASSIGNED,gs://folder/image4.jpg

Object Detection

See [Preparing your training data]() for more information.

A CSV file(s) with each line in format:

 ML_USE,GCS_FILE_PATH,\[LABEL\],(BOUNDING_BOX | ,,,,,,,)

• ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:

  • TRAIN - Rows in this file are used to train the model.
  • TEST - Rows in this file are used to test the model during training.
  • UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.

• GCS_FILE_PATH - The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image is assumed to be exhaustively labeled.

• LABEL - A label that identifies the object in the image specified by the BOUNDING_BOX.

• BOUNDING BOX - The vertices of an object in the example image. The minimum allowed BOUNDING_BOX edge length is 0.01, and no more than 500 BOUNDING_BOX instances per image are allowed (one BOUNDING_BOX per line). If an image has no looked for objects then it should be mentioned just once with no LABEL and the “,,,,,,,” in place of the BOUNDING_BOX.

Four sample rows:

 TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,,
 TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,,
 UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3
 TEST,gs://folder/im3.png,,,,,,,,,

AutoML Video Intelligence

Classification

See Preparing your training data for more information.

CSV file(s) with each line in format:

 ML_USE,GCS_FILE_PATH

For ML_USE, do not use VALIDATE.

GCS_FILE_PATH is the path to another .csv file that describes training example for a given ML_USE, using the following row format:

 GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,)

Here GCS_FILE_PATH leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.

TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time. Any segment of a video which has one or more labels on it, is considered a hard negative for all other labels. Any segment with no labels on it is considered to be unknown. If a whole video is unknown, then it should be mentioned just once with “,,” in place of LABEL, TIME_SEGMENT_START,TIME_SEGMENT_END.

Sample top level CSV file:

 TRAIN,gs://folder/train_videos.csv
 TEST,gs://folder/test_videos.csv
 UNASSIGNED,gs://folder/other_videos.csv

Sample rows of a CSV file for a particular ML_USE:

 gs://folder/video1.avi,car,120,180.000021
 gs://folder/video1.avi,bike,150,180.000021
 gs://folder/vid2.avi,car,0,60.5
 gs://folder/vid3.avi,,,

Object Tracking

See Preparing your training data for more information.

CSV file(s) with each line in format:

 ML_USE,GCS_FILE_PATH

For ML_USE, do not use VALIDATE.

GCS_FILE_PATH is the path to another .csv file that describes training example for a given ML_USE, using the following row format:

 GCS_FILE_PATH,LABEL,\[INSTANCE_ID\],TIMESTAMP,BOUNDING_BOX

or

 GCS_FILE_PATH,,,,,,,,,,

Here GCS_FILE_PATH leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. Providing INSTANCE_IDs can help to obtain a better model. When a specific labeled entity leaves the video frame, and shows up afterwards it is not required, albeit preferable, that the same INSTANCE_ID is given to it.

TIMESTAMP must be within the length of the video, the BOUNDING_BOX is assumed to be drawn on the closest video’s frame to the TIMESTAMP. Any mentioned by the TIMESTAMP frame is expected to be exhaustively labeled and no more than 500 BOUNDING_BOX-es per frame are allowed. If a whole video is unknown, then it should be mentioned just once with “,,,,,,,,,,” in place of LABEL, [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX.

Sample top level CSV file:

  TRAIN,gs://folder/train_videos.csv
  TEST,gs://folder/test_videos.csv
  UNASSIGNED,gs://folder/other_videos.csv

Seven sample rows of a CSV file for a particular ML_USE:

  gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9
  gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9
  gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3
  gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,,
  gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,,
  gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,,
  gs://folder/video2.avi,,,,,,,,,,,

AutoML Natural Language

Entity Extraction

See Preparing your training data for more information.

One or more CSV file(s) with each line in the following format:

 ML_USE,GCS_FILE_PATH

• ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:

  • TRAIN - Rows in this file are used to train the model.
  • TEST - Rows in this file are used to test the model during training.
  • UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing..

• GCS_FILE_PATH - a Identifies JSON Lines (.JSONL) file stored in Google Cloud Storage that contains in-line text in-line as documents for model training.

After the training data set has been determined from the TRAIN and UNASSIGNED CSV files, the training data is divided into train and validation data sets. 70% for training and 30% for validation.

For example:

 TRAIN,gs://folder/file1.jsonl
 VALIDATE,gs://folder/file2.jsonl
 TEST,gs://folder/file3.jsonl

In-line JSONL files

In-line .JSONL files contain, per line, a JSON document that wraps a [text_snippet][google.cloud.automl.v1.TextSnippet] field followed by one or more [annotations][google.cloud.automl.v1.AnnotationPayload] fields, which have display_name and text_extraction fields to describe the entity from the text snippet. Multiple JSON documents can be separated using line breaks (\n).

The supplied text must be annotated exhaustively. For example, if you include the text “horse”, but do not label it as “animal”, then “horse” is assumed to not be an “animal”.

Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded. ASCII is accepted as it is UTF-8 NFC encoded.

For example:

 {
   "text_snippet": {
     "content": "dog car cat"
   },
   "annotations": [
      {
        "display_name": "animal",
        "text_extraction": {
          "text_segment": {"start_offset": 0, "end_offset": 2}
       }
      },
      {
       "display_name": "vehicle",
        "text_extraction": {
          "text_segment": {"start_offset": 4, "end_offset": 6}
        }
      },
      {
        "display_name": "animal",
        "text_extraction": {
          "text_segment": {"start_offset": 8, "end_offset": 10}
        }
      }
  ]
 }\n
 {
    "text_snippet": {
      "content": "This dog is good."
    },
    "annotations": [
       {
         "display_name": "animal",
         "text_extraction": {
           "text_segment": {"start_offset": 5, "end_offset": 7}
         }
       }
    ]
 }

JSONL files that reference documents

.JSONL files contain, per line, a JSON document that wraps a input_config that contains the path to a source document. Multiple JSON documents can be separated using line breaks (\n).

Supported document extensions: .PDF, .TIF, .TIFF

For example:

 {
   "document": {
     "input_config": {
       "gcs_source": { "input_uris": \[ "gs://folder/document1.pdf" \]
       }
     }
   }
 }\n
 {
   "document": {
     "input_config": {
       "gcs_source": { "input_uris": \[ "gs://folder/document2.tif" \]
       }
     }
   }
 }

In-line JSONL files with document layout information

Note: You can only annotate documents using the UI. The format described below applies to annotated documents exported using the UI or exportData.

In-line .JSONL files for documents contain, per line, a JSON document that wraps a document field that provides the textual content of the document and the layout information.

For example:

 {
   "document": {
           "document_text": {
             "content": "dog car cat"
           }
           "layout": [
             {
               "text_segment": {
                 "start_offset": 0,
                 "end_offset": 11,
                },
                "page_number": 1,
                "bounding_poly": {
                   "normalized_vertices": [
                     {"x": 0.1, "y": 0.1},
                     {"x": 0.1, "y": 0.3},
                     {"x": 0.3, "y": 0.3},
                     {"x": 0.3, "y": 0.1},
                   ],
                 },
                 "text_segment_type": TOKEN,
             }
           ],
           "document_dimensions": {
             "width": 8.27,
             "height": 11.69,
             "unit": INCH,
           }
           "page_count": 3,
         },
         "annotations": [
           {
             "display_name": "animal",
             "text_extraction": {
               "text_segment": {"start_offset": 0, "end_offset": 3}
             }
           },
           {
             "display_name": "vehicle",
             "text_extraction": {
               "text_segment": {"start_offset": 4, "end_offset": 7}
             }
           },
           {
             "display_name": "animal",
             "text_extraction": {
               "text_segment": {"start_offset": 8, "end_offset": 11}
             }
           },
         ],

Classification

See Preparing your training data for more information.

One or more CSV file(s) with each line in the following format:

 ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,...

• ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:

  • TRAIN - Rows in this file are used to train the model.
  • TEST - Rows in this file are used to test the model during training.
  • UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.

• TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by “gs://”, it is treated as a GCS_FILE_PATH. Otherwise, if the content is enclosed in double quotes (“”), it is treated as a TEXT_SNIPPET. For GCS_FILE_PATH, the path must lead to a file with supported extension and UTF-8 encoding, for example, “gs://folder/content.txt” AutoML imports the file content as a text snippet. For TEXT_SNIPPET, AutoML imports the column content excluding quotes. In both cases, size of the content must be 10MB or less in size. For zip files, the size of each file inside the zip must be 10MB or less in size.

  For the MULTICLASS classification type, at most one LABEL is allowed.

  The ML_USE and LABEL columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP

A maximum of 100 unique labels are allowed per CSV row.

Sample rows:

 TRAIN,"They have bad food and very rude",RudeService,BadFood
 gs://folder/content.txt,SlowService
 TEST,gs://folder/document.pdf
 VALIDATE,gs://folder/text_files.zip,BadFood

Sentiment Analysis

See Preparing your training data for more information.

CSV file(s) with each line in format:

 ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT

• ML_USE - Identifies the data set that the current row (file) applies to. This value can be one of the following:

  • TRAIN - Rows in this file are used to train the model.
  • TEST - Rows in this file are used to test the model during training.
  • UNASSIGNED - Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.

• TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by “gs://”, it is treated as a GCS_FILE_PATH. Otherwise, if the content is enclosed in double quotes (“”), it is treated as a TEXT_SNIPPET. For GCS_FILE_PATH, the path must lead to a file with supported extension and UTF-8 encoding, for example, “gs://folder/content.txt” AutoML imports the file content as a text snippet. For TEXT_SNIPPET, AutoML imports the column content excluding quotes. In both cases, size of the content must be 128kB or less in size. For zip files, the size of each file inside the zip must be 128kB or less in size.

  The ML_USE and SENTIMENT columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP

• SENTIMENT - An integer between 0 and Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive). Describes the ordinal of the sentiment - higher value means a more positive sentiment. All the values are completely relative, i.e. neither 0 needs to mean a negative or neutral sentiment nor sentiment_max needs to mean a positive one - it is just required that 0 is the least positive sentiment in the data, and sentiment_max is the most positive one. The SENTIMENT shouldn’t be confused with “score” or “magnitude” from the previous Natural Language Sentiment Analysis API. All SENTIMENT values between 0 and sentiment_max must be represented in the imported data. On prediction the same 0 to sentiment_max range will be used. The difference between neighboring sentiment values needs not to be uniform, e.g. 1 and 2 may be similar whereas the difference between 2 and 3 may be large.

Sample rows:

 TRAIN,"@freewrytin this is way too good for your product",2
 gs://folder/content.txt,3
 TEST,gs://folder/document.pdf
 VALIDATE,gs://folder/text_files.zip,2

AutoML Tables

See Preparing your training data for more information.

You can use either [gcs_source][google.cloud.automl.v1.InputConfig.gcs_source] or [bigquery_source][google.cloud.automl.v1.InputConfig.bigquery_source]. All input is concatenated into a single [primary_table_spec_id][google.cloud.automl.v1.TablesDatasetMetadata.primary_table_spec_id]

For gcs_source:

CSV file(s), where the first row of the first file is the header, containing unique column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns.

Each .CSV file by itself must be 10GB or smaller, and their total size must be 100GB or smaller.

First three sample rows of a CSV file:

"Id","First Name","Last Name","Dob","Addresses"
"1","John","Doe","1968-01-22","\[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}\]"
"2","Jane","Doe","1980-10-16","\[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}\]}

For bigquery_source:

An URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller.

An imported table must have between 2 and 1,000 columns, inclusive, and between 1000 and 100,000,000 rows, inclusive. There are at most 5 import data running in parallel.

Input field definitions:

ML_USE : (“TRAIN” | “VALIDATE” | “TEST” | “UNASSIGNED”) Describes how the given example (file) should be used for model training. “UNASSIGNED” can be used when user has no preference.

GCS_FILE_PATH : The path to a file on Google Cloud Storage. For example, “gs://folder/image1.png”.

LABEL : A display name of an object on an image, video etc., e.g. “dog”. Must be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9. For each label an AnnotationSpec is created which display_name becomes the label; AnnotationSpecs are given back in predictions.

INSTANCE_ID : A positive integer that identifies a specific instance of a labeled entity on an example. Used e.g. to track two cars on a video while being able to tell apart which one is which.

BOUNDING_BOX : (VERTEX,VERTEX,VERTEX,VERTEX | VERTEX,,,VERTEX,,) A rectangle parallel to the frame of the example (image, video). If 4 vertices are given they are connected by edges in the order provided, if 2 are given they are recognized as diagonally opposite vertices of the rectangle.

VERTEX : (COORDINATE,COORDINATE) First coordinate is horizontal (x), the second is vertical (y).

COORDINATE : A float in 0 to 1 range, relative to total length of image or video in given dimension. For fractions the leading non-decimal 0 can be omitted (i.e. 0.3 = .3). Point 0,0 is in top left.

TIME_SEGMENT_START : (TIME_OFFSET) Expresses a beginning, inclusive, of a time segment within an example that has a time dimension (e.g. video).

TIME_SEGMENT_END : (TIME_OFFSET) Expresses an end, exclusive, of a time segment within n example that has a time dimension (e.g. video).

TIME_OFFSET : A number of seconds as measured from the start of an example (e.g. video). Fractions are allowed, up to a microsecond precision. “inf” is allowed, and it means the end of the example.

TEXT_SNIPPET : The content of a text snippet, UTF-8 encoded, enclosed within double quotes (“”).

DOCUMENT : A field that provides the textual content with document and the layout information.

Errors:

If any of the provided CSV files can’t be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and nothing is imported. Regardless of overall success or failure the per-row failures, up to a certain count cap, is listed in Operation.metadata.partial_failures.

Fields

params: BTreeMap<String, String>

Additional domain-specific parameters describing the semantic of the imported data, any string must be up to 25000 characters long.

AutoML Tables

schema_inference_version : (integer) This value must be supplied. The version of the algorithm to use for the initial inference of the column data types of the imported table. Allowed values: “1”.

source: Option<Source>

The source of the input.

Trait Implementations

impl Clone for InputConfig

fn clone(&self) -> InputConfig

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for InputConfig

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for InputConfig

fn default() -> Self

Returns the “default value” for a type.

impl Message for InputConfig

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited( &self, buf: &mut impl BufMut, ) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for InputConfig

fn eq(&self, other: &InputConfig) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for InputConfig