# Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. module Google module Cloud module AutoML module V1beta1 # Input configuration for ImportData Action. # # The format of input depends on dataset_metadata the Dataset into which # the import is happening has. As input source the # {Google::Cloud::AutoML::V1beta1::InputConfig#gcs_source 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: # # * For Image Classification: # CSV file(s) with each line in format: # ML_USE,GCS_FILE_PATH,LABEL,LABEL,... # GCS_FILE_PATH leads to image of up to 30MB in size. Supported # extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO # For 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 # # * For Image Object Detection: # CSV file(s) with each line in format: # ML_USE,GCS_FILE_PATH,(LABEL,BOUNDING_BOX | ,,,,,,,) # GCS_FILE_PATH leads to image of up to 30MB in size. Supported # extensions: .JPEG, .GIF, .PNG. # Each image is assumed to be exhaustively labeled. The minimum # allowed BOUNDING_BOX edge length is 0.01, and no more than 500 # BOUNDING_BOX-es per image are allowed (one BOUNDING_BOX is defined # per line). If an image has not yet been labeled, then it should be # mentioned just once with no LABEL and the ",,,,,,," in place of the # BOUNDING_BOX. For images which are known to not contain any # bounding boxes, they should be labelled explictly as # "NEGATIVE_IMAGE", followed by ",,,,,,," in place of the # BOUNDING_BOX. # 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,,,,,,,,, # TRAIN,gs://folder/im4.png,NEGATIVE_IMAGE,,,,,,,,, # # * For Video Classification: # CSV file(s) with each line in format: # ML_USE,GCS_FILE_PATH # where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH # should lead to another .csv file which describes examples that have # 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 end has to be after the start. 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 shuold 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,,, # # * For Video Object Tracking: # CSV file(s) with each line in format: # ML_USE,GCS_FILE_PATH # where ML_USE VALIDATE value should not be used. The GCS_FILE_PATH # should lead to another .csv file which describes examples that have # given ML_USE, using one of 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,,,,,,,,,,, # * For Text Extraction: # CSV file(s) with each line in format: # ML_USE,GCS_FILE_PATH # GCS_FILE_PATH leads to a .JSONL (that is, JSON Lines) file which # either imports text in-line or as documents. Any given # .JSONL file must be 100MB or smaller. # The in-line .JSONL file contains, per line, a proto that wraps a # TextSnippet proto (in json representation) followed by one or more # AnnotationPayload protos (called annotations), which have # display_name and text_extraction detail populated. The given text # is expected to be annotated exhaustively, for example, if you look # for animals and text contains "dolphin" that is not labeled, then # "dolphin" is assumed to not be an animal. Any given text snippet # content must be 10KB or smaller, and also be UTF-8 NFC encoded # (ASCII already is). # The document .JSONL file contains, per line, a proto that wraps a # Document proto. The Document proto must have either document_text # or input_config set. In document_text case, the Document proto may # also contain the spatial information of the document, including # layout, document dimension and page number. In input_config case, # only PDF documents are supported now, and each document may be up # to 2MB large. Currently, annotations on documents cannot be # specified at import. # Three sample CSV rows: # TRAIN,gs://folder/file1.jsonl # VALIDATE,gs://folder/file2.jsonl # TEST,gs://folder/file3.jsonl # Sample in-line JSON Lines file for entity extraction (presented here # with artificial line breaks, but the only actual line break is # denoted by \n).: # { # "document": { # "document_text": {"content": "dog cat"} # "layout": [ # { # "text_segment": { # "start_offset": 0, # "end_offset": 3, # }, # "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, # }, # { # "text_segment": { # "start_offset": 4, # "end_offset": 7, # }, # "page_number": 1, # "bounding_poly": { # "normalized_vertices": [ # {"x": 0.4, "y": 0.1}, # {"x": 0.4, "y": 0.3}, # {"x": 0.8, "y": 0.3}, # {"x": 0.8, "y": 0.1}, # ], # }, # "text_segment_type": TOKEN, # } # # ], # "document_dimensions": { # "width": 8.27, # "height": 11.69, # "unit": INCH, # } # "page_count": 1, # }, # "annotations": [ # { # "display_name": "animal", # "text_extraction": {"text_segment": {"start_offset": 0, # "end_offset": 3}} # }, # { # "display_name": "animal", # "text_extraction": {"text_segment": {"start_offset": 4, # "end_offset": 7}} # } # ], # }\n # { # "text_snippet": { # "content": "This dog is good." # }, # "annotations": [ # { # "display_name": "animal", # "text_extraction": { # "text_segment": {"start_offset": 5, "end_offset": 8} # } # } # ] # } # 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 Text Classification: # CSV file(s) with each line in format: # ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,... # TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If # the column content is a valid gcs file path, i.e. prefixed by # "gs://", it will be treated as a GCS_FILE_PATH, else if the content # is enclosed within double quotes (""), it is # treated as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path # must lead to a .txt file with UTF-8 encoding, for example, # "gs://folder/content.txt", and the content in it is extracted # as a text snippet. In TEXT_SNIPPET case, the column content # excluding quotes is treated as to be imported text snippet. In # both cases, the text snippet/file size must be within 128kB. # Maximum 100 unique labels are allowed per CSV row. # Sample rows: # TRAIN,"They have bad food and very rude",RudeService,BadFood # TRAIN,gs://folder/content.txt,SlowService # TEST,"Typically always bad service there.",RudeService # VALIDATE,"Stomach ache to go.",BadFood # # * For Text Sentiment: # CSV file(s) with each line in format: # ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENT # TEXT_SNIPPET and GCS_FILE_PATH are distinguished by a pattern. If # the column content is a valid gcs file path, that is, prefixed by # "gs://", it is treated as a GCS_FILE_PATH, otherwise it is treated # as a TEXT_SNIPPET. In the GCS_FILE_PATH case, the path # must lead to a .txt file with UTF-8 encoding, for example, # "gs://folder/content.txt", and the content in it is extracted # as a text snippet. In TEXT_SNIPPET case, the column content itself # is treated as to be imported text snippet. In both cases, the # text snippet must be up to 500 characters long. # Sample rows: # TRAIN,"@freewrytin this is way too good for your product",2 # TRAIN,"I need this product so bad",3 # TEST,"Thank you for this product.",4 # VALIDATE,gs://folder/content.txt,2 # # * For Tables: # Either # {Google::Cloud::AutoML::V1beta1::InputConfig#gcs_source gcs_source} or # # {Google::Cloud::AutoML::V1beta1::InputConfig#bigquery_source bigquery_source} # can be used. All inputs is concatenated into a single # # {Google::Cloud::AutoML::V1beta1::TablesDatasetMetadata#primary_table_name primary_table} # 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. # 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 = A path to file on GCS, e.g. "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 # 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. # TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within # double quotes (""). # 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 # huge. # # 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. # @!attribute [rw] gcs_source # @return [Google::Cloud::AutoML::V1beta1::GcsSource] # The Google Cloud Storage location for the input content. # In ImportData, the gcs_source points to a csv with structure described in # the comment. # @!attribute [rw] bigquery_source # @return [Google::Cloud::AutoML::V1beta1::BigQuerySource] # The BigQuery location for the input content. # @!attribute [rw] params # @return [Hash{String => String}] # Additional domain-specific parameters describing the semantic of the # imported data, any string must be up to 25000 # characters long. # # * For Tables: # `schema_inference_version` - (integer) Required. The version of the # algorithm that should be used for the initial inference of the # schema (columns' DataTypes) of the table the data is being imported # into. Allowed values: "1". class InputConfig; end # Input configuration for BatchPredict Action. # # The format of input depends on the ML problem of the model used for # prediction. As input source the # {Google::Cloud::AutoML::V1beta1::InputConfig#gcs_source 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 # {Google::Cloud::AutoML::V1beta1::InputConfig#gcs_source gcs_source} or # # {Google::Cloud::AutoML::V1beta1::InputConfig#bigquery_source 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 # # {Google::Cloud::AutoML::V1beta1::TablesModelMetadata#input_feature_column_specs input_feature_column_specs'} # # {Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name display_name-s} # (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 # # {Google::Cloud::AutoML::V1beta1::TablesModelMetadata#prediction_type prediction_type}: # all columns having # # {Google::Cloud::AutoML::V1beta1::ColumnSpec::ForecastingMetadata::ColumnType TIME_SERIES_AVAILABLE_PAST_ONLY} # 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 # # {Google::Cloud::AutoML::V1beta1::TablesModelMetadata#input_feature_column_specs input_feature_column_specs'} # # {Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name display_name-s} # (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 # # {Google::Cloud::AutoML::V1beta1::TablesModelMetadata#prediction_type prediction_type}: # all columns having # # {Google::Cloud::AutoML::V1beta1::ColumnSpec::ForecastingMetadata::ColumnType TIME_SERIES_AVAILABLE_PAST_ONLY} # 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. # @!attribute [rw] gcs_source # @return [Google::Cloud::AutoML::V1beta1::GcsSource] # The Google Cloud Storage location for the input content. # @!attribute [rw] bigquery_source # @return [Google::Cloud::AutoML::V1beta1::BigQuerySource] # The BigQuery location for the input content. class BatchPredictInputConfig; end # Input configuration of a {Google::Cloud::AutoML::V1beta1::Document Document}. # @!attribute [rw] gcs_source # @return [Google::Cloud::AutoML::V1beta1::GcsSource] # The Google Cloud Storage location of the document file. Only a single path # should be given. # Max supported size: 512MB. # Supported extensions: .PDF. class DocumentInputConfig; end # * For Translation: # CSV file `translation.csv`, with each line in format: # ML_USE,GCS_FILE_PATH # GCS_FILE_PATH leads to a .TSV file which describes examples that have # given ML_USE, using the following row format per line: # TEXT_SNIPPET (in source language) \t TEXT_SNIPPET (in target # language) # # * For Tables: # Output depends on whether the dataset was imported from GCS or # BigQuery. # GCS case: # # {Google::Cloud::AutoML::V1beta1::OutputConfig#gcs_destination gcs_destination} # must be set. Exported are CSV file(s) `tables_1.csv`, # `tables_2.csv`,...,`tables_N.csv` with each having as header line # the table's column names, and all other lines contain values for # the header columns. # BigQuery case: # # {Google::Cloud::AutoML::V1beta1::OutputConfig#bigquery_destination bigquery_destination} # pointing to a BigQuery project must be set. In the given project a # new dataset will be created with name # # `export_data__` # where will be made # BigQuery-dataset-name compatible (e.g. most special characters will # become underscores), and timestamp will be in # YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In that # dataset a new table called `primary_table` will be created, and # filled with precisely the same data as this obtained on import. # @!attribute [rw] gcs_destination # @return [Google::Cloud::AutoML::V1beta1::GcsDestination] # The Google Cloud Storage location where the output is to be written to. # For Image Object Detection, Text Extraction, Video Classification and # Tables, in the given directory a new directory will be created with name: # export_data-- where # timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export # output will be written into that directory. # @!attribute [rw] bigquery_destination # @return [Google::Cloud::AutoML::V1beta1::BigQueryDestination] # The BigQuery location where the output is to be written to. class OutputConfig; end # Output configuration for BatchPredict Action. # # As destination the # # {Google::Cloud::AutoML::V1beta1::BatchPredictOutputConfig#gcs_destination gcs_destination} # must be set unless specified otherwise for a domain. If gcs_destination is # set then in the given directory a new directory is created. Its name # will be # "prediction--", # where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents # of it depends on the ML problem the predictions are made for. # # * For Image Classification: # In the created directory files `image_classification_1.jsonl`, # `image_classification_2.jsonl`,...,`image_classification_N.jsonl` # will be created, where N may be 1, and depends on the # total number of the successfully predicted images and annotations. # A single image will be listed only once with all its annotations, # and its annotations will never be split across files. # Each .JSONL file will contain, per line, a JSON representation of a # proto that wraps image's "ID" : "" followed by a list of # zero or more AnnotationPayload protos (called annotations), which # have classification detail populated. # If prediction for any image failed (partially or completely), then an # additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl` # files will be created (N depends on total number of failed # predictions). These files will have a JSON representation of a proto # that wraps the same "ID" : "" but here followed by # exactly one # # [`google.rpc.Status`](https: # //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) # containing only `code` and `message`fields. # # * For Image Object Detection: # In the created directory files `image_object_detection_1.jsonl`, # `image_object_detection_2.jsonl`,...,`image_object_detection_N.jsonl` # will be created, where N may be 1, and depends on the # total number of the successfully predicted images and annotations. # Each .JSONL file will contain, per line, a JSON representation of a # proto that wraps image's "ID" : "" followed by a list of # zero or more AnnotationPayload protos (called annotations), which # have image_object_detection detail populated. A single image will # be listed only once with all its annotations, and its annotations # will never be split across files. # If prediction for any image failed (partially or completely), then # additional `errors_1.jsonl`, `errors_2.jsonl`,..., `errors_N.jsonl` # files will be created (N depends on total number of failed # predictions). These files will have a JSON representation of a proto # that wraps the same "ID" : "" but here followed by # exactly one # # [`google.rpc.Status`](https: # //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) # containing only `code` and `message`fields. # * For Video Classification: # In the created directory a video_classification.csv file, and a .JSON # file per each video classification requested in the input (i.e. each # line in given CSV(s)), will be created. # # The format of video_classification.csv is: # # GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS # where: # GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 # the prediction input lines (i.e. video_classification.csv has # precisely the same number of lines as the prediction input had.) # JSON_FILE_NAME = Name of .JSON file in the output directory, which # contains prediction responses for the video time segment. # STATUS = "OK" if prediction completed successfully, or an error code # with message otherwise. If STATUS is not "OK" then the .JSON file # for that line may not exist or be empty. # # Each .JSON file, assuming STATUS is "OK", will contain a list of # AnnotationPayload protos in JSON format, which are the predictions # for the video time segment the file is assigned to in the # video_classification.csv. All AnnotationPayload protos will have # video_classification field set, and will be sorted by # video_classification.type field (note that the returned types are # governed by `classifaction_types` parameter in # {PredictService::BatchPredictRequest#params}). # # * For Video Object Tracking: # In the created directory a video_object_tracking.csv file will be # created, and multiple files video_object_trackinng_1.json, # video_object_trackinng_2.json,..., video_object_trackinng_N.json, # where N is the number of requests in the input (i.e. the number of # lines in given CSV(s)). # # The format of video_object_tracking.csv is: # # GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS # where: # GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 # the prediction input lines (i.e. video_object_tracking.csv has # precisely the same number of lines as the prediction input had.) # JSON_FILE_NAME = Name of .JSON file in the output directory, which # contains prediction responses for the video time segment. # STATUS = "OK" if prediction completed successfully, or an error # code with message otherwise. If STATUS is not "OK" then the .JSON # file for that line may not exist or be empty. # # Each .JSON file, assuming STATUS is "OK", will contain a list of # AnnotationPayload protos in JSON format, which are the predictions # for each frame of the video time segment the file is assigned to in # video_object_tracking.csv. All AnnotationPayload protos will have # video_object_tracking field set. # * For Text Classification: # In the created directory files `text_classification_1.jsonl`, # `text_classification_2.jsonl`,...,`text_classification_N.jsonl` # will be created, where N may be 1, and depends on the # total number of inputs and annotations found. # # Each .JSONL file will contain, per line, a JSON representation of a # proto that wraps input text snippet or input text file and a list of # zero or more AnnotationPayload protos (called annotations), which # have classification detail populated. A single text snippet or file # will be listed only once with all its annotations, and its # annotations will never be split across files. # # If prediction for any text snippet or file failed (partially or # completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,..., # `errors_N.jsonl` files will be created (N depends on total number of # failed predictions). These files will have a JSON representation of a # proto that wraps input text snippet or input text file followed by # exactly one # # [`google.rpc.Status`](https: # //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) # containing only `code` and `message`. # # * For Text Sentiment: # In the created directory files `text_sentiment_1.jsonl`, # `text_sentiment_2.jsonl`,...,`text_sentiment_N.jsonl` # will be created, where N may be 1, and depends on the # total number of inputs and annotations found. # # Each .JSONL file will contain, per line, a JSON representation of a # proto that wraps input text snippet or input text file and a list of # zero or more AnnotationPayload protos (called annotations), which # have text_sentiment detail populated. A single text snippet or file # will be listed only once with all its annotations, and its # annotations will never be split across files. # # If prediction for any text snippet or file failed (partially or # completely), then additional `errors_1.jsonl`, `errors_2.jsonl`,..., # `errors_N.jsonl` files will be created (N depends on total number of # failed predictions). These files will have a JSON representation of a # proto that wraps input text snippet or input text file followed by # exactly one # # [`google.rpc.Status`](https: # //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) # containing only `code` and `message`. # # * For Text Extraction: # In the created directory files `text_extraction_1.jsonl`, # `text_extraction_2.jsonl`,...,`text_extraction_N.jsonl` # will be created, where N may be 1, and depends on the # total number of inputs and annotations found. # The contents of these .JSONL file(s) depend on whether the input # used inline text, or documents. # If input was inline, then each .JSONL file will contain, per line, # a JSON representation of a proto that wraps given in request text # snippet's "id" (if specified), followed by input text snippet, # and a list of zero or more # AnnotationPayload protos (called annotations), which have # text_extraction detail populated. A single text snippet will be # listed only once with all its annotations, and its annotations will # never be split across files. # If input used documents, then each .JSONL file will contain, per # line, a JSON representation of a proto that wraps given in request # document proto, followed by its OCR-ed representation in the form # of a text snippet, finally followed by a list of zero or more # AnnotationPayload protos (called annotations), which have # text_extraction detail populated and refer, via their indices, to # the OCR-ed text snippet. A single document (and its text snippet) # will be listed only once with all its annotations, and its # annotations will never be split across files. # If prediction for any text snippet failed (partially or completely), # then additional `errors_1.jsonl`, `errors_2.jsonl`,..., # `errors_N.jsonl` files will be created (N depends on total number of # failed predictions). These files will have a JSON representation of a # proto that wraps either the "id" : "" (in case of inline) # or the document proto (in case of document) but here followed by # exactly one # # [`google.rpc.Status`](https: # //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) # containing only `code` and `message`. # # * For Tables: # Output depends on whether # # {Google::Cloud::AutoML::V1beta1::BatchPredictOutputConfig#gcs_destination gcs_destination} # or # # {Google::Cloud::AutoML::V1beta1::BatchPredictOutputConfig#bigquery_destination bigquery_destination} # is set (either is allowed). # GCS case: # In the created directory files `tables_1.csv`, `tables_2.csv`,..., # `tables_N.csv` will be created, where N may be 1, and depends on # the total number of the successfully predicted rows. # For all CLASSIFICATION # # {Google::Cloud::AutoML::V1beta1::TablesModelMetadata#prediction_type prediction_type-s}: # Each .csv file will contain a header, listing all columns' # # {Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name display_name-s} # given on input followed by M target column names in the format of # # "<{Google::Cloud::AutoML::V1beta1::TablesModelMetadata#target_column_spec target_column_specs} # # {Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name display_name}>__score" where M is the number of distinct target values, # i.e. number of distinct values in the target column of the table # used to train the model. Subsequent lines will contain the # respective values of successfully predicted rows, with the last, # i.e. the target, columns having the corresponding prediction # {Google::Cloud::AutoML::V1beta1::TablesAnnotation#score scores}. # For REGRESSION and FORECASTING # # {Google::Cloud::AutoML::V1beta1::TablesModelMetadata#prediction_type prediction_type-s}: # Each .csv file will contain a header, listing all columns' # {Google::Cloud::AutoML::V1beta1::Display_name display_name-s} given # on input followed by the predicted target column with name in the # format of # # "predicted_<{Google::Cloud::AutoML::V1beta1::TablesModelMetadata#target_column_spec target_column_specs} # # {Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name display_name}>" # Subsequent lines will contain the respective values of # successfully predicted rows, with the last, i.e. the target, # column having the predicted target value. # If prediction for any rows failed, then an additional # `errors_1.csv`, `errors_2.csv`,..., `errors_N.csv` will be # created (N depends on total number of failed rows). These files # will have analogous format as `tables_*.csv`, but always with a # single target column having # # [`google.rpc.Status`](https: # //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) # represented as a JSON string, and containing only `code` and # `message`. # BigQuery case: # # {Google::Cloud::AutoML::V1beta1::OutputConfig#bigquery_destination bigquery_destination} # pointing to a BigQuery project must be set. In the given project a # new dataset will be created with name # `prediction__` # where will be made # BigQuery-dataset-name compatible (e.g. most special characters will # become underscores), and timestamp will be in # YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset # two tables will be created, `predictions`, and `errors`. # The `predictions` table's column names will be the input columns' # # {Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name display_name-s} # followed by the target column with name in the format of # # "predicted_<{Google::Cloud::AutoML::V1beta1::TablesModelMetadata#target_column_spec target_column_specs} # # {Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name display_name}>" # The input feature columns will contain the respective values of # successfully predicted rows, with the target column having an # ARRAY of # # {Google::Cloud::AutoML::V1beta1::AnnotationPayload AnnotationPayloads}, # represented as STRUCT-s, containing # {Google::Cloud::AutoML::V1beta1::TablesAnnotation TablesAnnotation}. # The `errors` table contains rows for which the prediction has # failed, it has analogous input columns while the target column name # is in the format of # # "errors_<{Google::Cloud::AutoML::V1beta1::TablesModelMetadata#target_column_spec target_column_specs} # # {Google::Cloud::AutoML::V1beta1::ColumnSpec#display_name display_name}>", # and as a value has # # [`google.rpc.Status`](https: # //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) # represented as a STRUCT, and containing only `code` and `message`. # @!attribute [rw] gcs_destination # @return [Google::Cloud::AutoML::V1beta1::GcsDestination] # The Google Cloud Storage location of the directory where the output is to # be written to. # @!attribute [rw] bigquery_destination # @return [Google::Cloud::AutoML::V1beta1::BigQueryDestination] # The BigQuery location where the output is to be written to. class BatchPredictOutputConfig; end # Output configuration for ModelExport Action. # @!attribute [rw] gcs_destination # @return [Google::Cloud::AutoML::V1beta1::GcsDestination] # The Google Cloud Storage location where the model is to be written to. # This location may only be set for the following model formats: # "tflite", "edgetpu_tflite", "core_ml", "docker". # # Under the directory given as the destination a new one with name # "model-export--", # where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format, # will be created. Inside the model and any of its supporting files # will be written. # @!attribute [rw] gcr_destination # @return [Google::Cloud::AutoML::V1beta1::GcrDestination] # The GCR location where model image is to be pushed to. This location # may only be set for the following model formats: # "docker". # # The model image will be created under the given URI. # @!attribute [rw] model_format # @return [String] # The format in which the model must be exported. The available, and default, # formats depend on the problem and model type (if given problem and type # combination doesn't have a format listed, it means its models are not # exportable): # # * For Image Classification mobile-low-latency-1, mobile-versatile-1, # mobile-high-accuracy-1: # "tflite" (default), "edgetpu_tflite", "tf_saved_model", "docker". # # * For Image Classification mobile-core-ml-low-latency-1, # mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1: # "core_ml" (default). # Formats description: # # * tflite - Used for Android mobile devices. # * edgetpu_tflite - Used for [Edge TPU](https://cloud.google.com/edge-tpu/) # devices. # * tf_saved_model - A tensorflow model in SavedModel format. # * docker - Used for Docker containers. Use the params field to customize # the container. The container is verified to work correctly on # ubuntu 16.04 operating system. See more at # [containers # # quickstart](https: # //cloud.google.com/vision/automl/docs/containers-gcs-quickstart) # * core_ml - Used for iOS mobile devices. # @!attribute [rw] params # @return [Hash{String => String}] # Additional model-type and format specific parameters describing the # requirements for the to be exported model files, any string must be up to # 25000 characters long. # # * For `docker` format: # `cpu_architecture` - (string) "x86_64" (default). # `gpu_architecture` - (string) "none" (default), "nvidia". class ModelExportOutputConfig; end # Output configuration for ExportEvaluatedExamples Action. Note that this call # is available only for 30 days since the moment the model was evaluated. # The output depends on the domain, as follows (note that only examples from # the TEST set are exported): # # * For Tables: # # {Google::Cloud::AutoML::V1beta1::OutputConfig#bigquery_destination bigquery_destination} # pointing to a BigQuery project must be set. In the given project a # new dataset will be created with name # # `export_evaluated_examples__` # where will be made BigQuery-dataset-name # compatible (e.g. most special characters will become underscores), # and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" # format. In the dataset an `evaluated_examples` table will be # created. It will have all the same columns as the # # {Google::Cloud::AutoML::V1beta1::TablesDatasetMetadata#primary_table_spec_id primary_table} # of the # {Google::Cloud::AutoML::V1beta1::Model#dataset_id dataset} from which # the model was created, as they were at the moment of model's # evaluation (this includes the target column with its ground # truth), followed by a column called "predicted_". That # last column will contain the model's prediction result for each # respective row, given as ARRAY of # {Google::Cloud::AutoML::V1beta1::AnnotationPayload AnnotationPayloads}, # represented as STRUCT-s, containing # {Google::Cloud::AutoML::V1beta1::TablesAnnotation TablesAnnotation}. # @!attribute [rw] bigquery_destination # @return [Google::Cloud::AutoML::V1beta1::BigQueryDestination] # The BigQuery location where the output is to be written to. class ExportEvaluatedExamplesOutputConfig; end # The Google Cloud Storage location for the input content. # @!attribute [rw] input_uris # @return [Array] # Required. Google Cloud Storage URIs to input files, up to 2000 characters # long. Accepted forms: # * Full object path, e.g. gs://bucket/directory/object.csv class GcsSource; end # The BigQuery location for the input content. # @!attribute [rw] input_uri # @return [String] # Required. BigQuery URI to a table, up to 2000 characters long. # Accepted forms: # * BigQuery path e.g. bq://projectId.bqDatasetId.bqTableId class BigQuerySource; end # The Google Cloud Storage location where the output is to be written to. # @!attribute [rw] output_uri_prefix # @return [String] # Required. Google Cloud Storage URI to output directory, up to 2000 # characters long. # Accepted forms: # * Prefix path: gs://bucket/directory # The requesting user must have write permission to the bucket. # The directory is created if it doesn't exist. class GcsDestination; end # The BigQuery location for the output content. # @!attribute [rw] output_uri # @return [String] # Required. BigQuery URI to a project, up to 2000 characters long. # Accepted forms: # * BigQuery path e.g. bq://projectId class BigQueryDestination; end # The GCR location where the image must be pushed to. # @!attribute [rw] output_uri # @return [String] # Required. Google Contained Registry URI of the new image, up to 2000 # characters long. See # # https: # //cloud.google.com/container-registry/do # // cs/pushing-and-pulling#pushing_an_image_to_a_registry # Accepted forms: # * [HOSTNAME]/[PROJECT-ID]/[IMAGE] # * [HOSTNAME]/[PROJECT-ID]/[IMAGE]:[TAG] # # The requesting user must have permission to push images the project. class GcrDestination; end end end end end