Class BatchPredictInputConfig

  • All Implemented Interfaces:
    BatchPredictInputConfigOrBuilder, com.google.protobuf.Message, com.google.protobuf.MessageLite, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Serializable

    public final class BatchPredictInputConfig
    extends com.google.protobuf.GeneratedMessageV3
    implements BatchPredictInputConfigOrBuilder
     Input configuration for BatchPredict Action.
    
     The format of input depends on the ML problem of the model used for
     prediction. As input source the
     [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source]
     is expected, unless specified otherwise.
    
     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:
    
      *  For Image Classification:
             CSV file(s) with each line having just a single column:
               GCS_FILE_PATH
               which leads to image of up to 30MB in size. Supported
               extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in
               the Batch predict output.
             Three sample rows:
               gs://folder/image1.jpeg
               gs://folder/image2.gif
               gs://folder/image3.png
    
      *  For Image Object Detection:
             CSV file(s) with each line having just a single column:
               GCS_FILE_PATH
               which leads to image of up to 30MB in size. Supported
               extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in
               the Batch predict output.
             Three sample rows:
               gs://folder/image1.jpeg
               gs://folder/image2.gif
               gs://folder/image3.png
      *  For Video Classification:
             CSV file(s) with each line in format:
               GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END
               GCS_FILE_PATH leads to 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 end has to be after the start.
             Three sample rows:
               gs://folder/video1.mp4,10,40
               gs://folder/video1.mp4,20,60
               gs://folder/vid2.mov,0,inf
    
      *  For Video Object Tracking:
             CSV file(s) with each line in format:
               GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END
               GCS_FILE_PATH leads to 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 end has to be after the start.
             Three sample rows:
               gs://folder/video1.mp4,10,240
               gs://folder/video1.mp4,300,360
               gs://folder/vid2.mov,0,inf
      *  For Text Classification:
             CSV file(s) with each line having just a single column:
               GCS_FILE_PATH | TEXT_SNIPPET
             Any given text file can have size upto 128kB.
             Any given text snippet content must have 60,000 characters or less.
             Three sample rows:
               gs://folder/text1.txt
               "Some text content to predict"
               gs://folder/text3.pdf
             Supported file extensions: .txt, .pdf
    
      *  For Text Sentiment:
             CSV file(s) with each line having just a single column:
               GCS_FILE_PATH | TEXT_SNIPPET
             Any given text file can have size upto 128kB.
             Any given text snippet content must have 500 characters or less.
             Three sample rows:
               gs://folder/text1.txt
               "Some text content to predict"
               gs://folder/text3.pdf
             Supported file extensions: .txt, .pdf
    
      * For Text Extraction
             .JSONL (i.e. JSON Lines) file(s) which either provide text in-line or
             as documents (for a single BatchPredict call only one of the these
             formats may be used).
             The in-line .JSONL file(s) contain per line a proto that
               wraps a temporary user-assigned TextSnippet ID (string up to 2000
               characters long) called "id", a TextSnippet proto (in
               json representation) and zero or more TextFeature protos. Any given
               text snippet content must have 30,000 characters or less, and also
               be UTF-8 NFC encoded (ASCII already is). The IDs provided should be
               unique.
             The document .JSONL file(s) contain, per line, a proto that wraps a
               Document proto with input_config set. Only PDF documents are
               supported now, and each document must be up to 2MB large.
             Any given .JSONL file must be 100MB or smaller, and no more than 20
             files may be given.
             Sample in-line JSON Lines file (presented here with artificial line
             breaks, but the only actual line break is denoted by \n):
               {
                 "id": "my_first_id",
                 "text_snippet": { "content": "dog car cat"},
                 "text_features": [
                   {
                     "text_segment": {"start_offset": 4, "end_offset": 6},
                     "structural_type": PARAGRAPH,
                     "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},
                       ]
                     },
                   }
                 ],
               }\n
               {
                 "id": "2",
                 "text_snippet": {
                   "content": "An elaborate content",
                   "mime_type": "text/plain"
                 }
               }
             Sample document JSON Lines file (presented here with artificial line
             breaks, but the only actual line break is denoted by \n).:
               {
                 "document": {
                   "input_config": {
                     "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ]
                     }
                   }
                 }
               }\n
               {
                 "document": {
                   "input_config": {
                     "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ]
                     }
                   }
                 }
               }
    
      *  For Tables:
             Either
             [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] or
    
     [bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source].
             GCS case:
               CSV file(s), each by itself 10GB or smaller and total size must be
               100GB or smaller, where first file must have a header containing
               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.
               The column names must contain the model's
    
     [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
    
     [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
               (order doesn't matter). The columns corresponding to the model's
               input feature column specs must contain values compatible with the
               column spec's data types. Prediction on all the rows, i.e. the CSV
               lines, will be attempted. For FORECASTING
    
     [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
               all columns having
    
     [TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType]
               type will be ignored.
               First three sample rows of a CSV file:
                 "First Name","Last Name","Dob","Addresses"
    
     "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"}]"
    
     "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"}]}
             BigQuery case:
               An URI of a BigQuery table. The user data size of the BigQuery
               table must be 100GB or smaller.
               The column names must contain the model's
    
     [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
    
     [display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name]
               (order doesn't matter). The columns corresponding to the model's
               input feature column specs must contain values compatible with the
               column spec's data types. Prediction on all the rows of the table
               will be attempted. For FORECASTING
    
     [prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]:
               all columns having
    
     [TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType]
               type will be ignored.
    
      Definitions:
      GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/video.avi".
      TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within
                     double quotes ("")
      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
                         an 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.
    
      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
      prediction does not happen. Regardless of overall success or failure the
      per-row failures, up to a certain count cap, will be listed in
      Operation.metadata.partial_failures.
     
    Protobuf type google.cloud.automl.v1beta1.BatchPredictInputConfig
    See Also:
    Serialized Form
    • Field Detail

      • GCS_SOURCE_FIELD_NUMBER

        public static final int GCS_SOURCE_FIELD_NUMBER
        See Also:
        Constant Field Values
      • BIGQUERY_SOURCE_FIELD_NUMBER

        public static final int BIGQUERY_SOURCE_FIELD_NUMBER
        See Also:
        Constant Field Values
    • Method Detail

      • newInstance

        protected Object newInstance​(com.google.protobuf.GeneratedMessageV3.UnusedPrivateParameter unused)
        Overrides:
        newInstance in class com.google.protobuf.GeneratedMessageV3
      • getDescriptor

        public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()
      • internalGetFieldAccessorTable

        protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
        Specified by:
        internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3
      • hasGcsSource

        public boolean hasGcsSource()
         The Google Cloud Storage location for the input content.
         
        .google.cloud.automl.v1beta1.GcsSource gcs_source = 1;
        Specified by:
        hasGcsSource in interface BatchPredictInputConfigOrBuilder
        Returns:
        Whether the gcsSource field is set.
      • hasBigquerySource

        public boolean hasBigquerySource()
         The BigQuery location for the input content.
         
        .google.cloud.automl.v1beta1.BigQuerySource bigquery_source = 2;
        Specified by:
        hasBigquerySource in interface BatchPredictInputConfigOrBuilder
        Returns:
        Whether the bigquerySource field is set.
      • isInitialized

        public final boolean isInitialized()
        Specified by:
        isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
        Overrides:
        isInitialized in class com.google.protobuf.GeneratedMessageV3
      • writeTo

        public void writeTo​(com.google.protobuf.CodedOutputStream output)
                     throws IOException
        Specified by:
        writeTo in interface com.google.protobuf.MessageLite
        Overrides:
        writeTo in class com.google.protobuf.GeneratedMessageV3
        Throws:
        IOException
      • getSerializedSize

        public int getSerializedSize()
        Specified by:
        getSerializedSize in interface com.google.protobuf.MessageLite
        Overrides:
        getSerializedSize in class com.google.protobuf.GeneratedMessageV3
      • equals

        public boolean equals​(Object obj)
        Specified by:
        equals in interface com.google.protobuf.Message
        Overrides:
        equals in class com.google.protobuf.AbstractMessage
      • hashCode

        public int hashCode()
        Specified by:
        hashCode in interface com.google.protobuf.Message
        Overrides:
        hashCode in class com.google.protobuf.AbstractMessage
      • parseFrom

        public static BatchPredictInputConfig parseFrom​(ByteBuffer data)
                                                 throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException
      • parseFrom

        public static BatchPredictInputConfig parseFrom​(ByteBuffer data,
                                                        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                                 throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException
      • parseFrom

        public static BatchPredictInputConfig parseFrom​(com.google.protobuf.ByteString data)
                                                 throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException
      • parseFrom

        public static BatchPredictInputConfig parseFrom​(com.google.protobuf.ByteString data,
                                                        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                                 throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException
      • parseFrom

        public static BatchPredictInputConfig parseFrom​(byte[] data)
                                                 throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException
      • parseFrom

        public static BatchPredictInputConfig parseFrom​(byte[] data,
                                                        com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                                 throws com.google.protobuf.InvalidProtocolBufferException
        Throws:
        com.google.protobuf.InvalidProtocolBufferException
      • newBuilderForType

        public BatchPredictInputConfig.Builder newBuilderForType()
        Specified by:
        newBuilderForType in interface com.google.protobuf.Message
        Specified by:
        newBuilderForType in interface com.google.protobuf.MessageLite
      • toBuilder

        public BatchPredictInputConfig.Builder toBuilder()
        Specified by:
        toBuilder in interface com.google.protobuf.Message
        Specified by:
        toBuilder in interface com.google.protobuf.MessageLite
      • newBuilderForType

        protected BatchPredictInputConfig.Builder newBuilderForType​(com.google.protobuf.GeneratedMessageV3.BuilderParent parent)
        Specified by:
        newBuilderForType in class com.google.protobuf.GeneratedMessageV3
      • getParserForType

        public com.google.protobuf.Parser<BatchPredictInputConfig> getParserForType()
        Specified by:
        getParserForType in interface com.google.protobuf.Message
        Specified by:
        getParserForType in interface com.google.protobuf.MessageLite
        Overrides:
        getParserForType in class com.google.protobuf.GeneratedMessageV3
      • getDefaultInstanceForType

        public BatchPredictInputConfig getDefaultInstanceForType()
        Specified by:
        getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
        Specified by:
        getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder