AutoML Vision
* Classification
* One or more CSV files where each line is a single column:
* GCS_FILE_PATH
* The Google Cloud Storage location of an image of up to
* 30MB in size. Supported extensions: .JPEG, .GIF, .PNG.
* This path is treated as the ID in the batch predict output.
* Sample rows:
* gs://folder/image1.jpeg
* gs://folder/image2.gif
* gs://folder/image3.png
* Object Detection
* One or more CSV files where each line is a single column:
* GCS_FILE_PATH
* The Google Cloud Storage location of an image of up to
* 30MB in size. Supported extensions: .JPEG, .GIF, .PNG.
* This path is treated as the ID in the batch predict output.
* Sample rows:
* gs://folder/image1.jpeg
* gs://folder/image2.gif
* gs://folder/image3.png
*
*
* AutoML Video Intelligence
* Classification
* One or more CSV files where each line is a single column:
* GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END
* `GCS_FILE_PATH` is the Google Cloud Storage location of video up to 50GB in
* size and up to 3h in duration 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.
* Sample rows:
* gs://folder/video1.mp4,10,40
* gs://folder/video1.mp4,20,60
* gs://folder/vid2.mov,0,inf
* Object Tracking
* One or more CSV files where each line is a single column:
* GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END
* `GCS_FILE_PATH` is the Google Cloud Storage location of video up to 50GB in
* size and up to 3h in duration 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.
* Sample rows:
* gs://folder/video1.mp4,10,40
* gs://folder/video1.mp4,20,60
* gs://folder/vid2.mov,0,inf
*
*
* AutoML Natural Language
* Classification
* One or more CSV files where each line is a single column:
* GCS_FILE_PATH
* `GCS_FILE_PATH` is the Google Cloud Storage location of a text file.
* Supported file extensions: .TXT, .PDF, .TIF, .TIFF
* Text files can be no larger than 10MB in size.
* Sample rows:
* gs://folder/text1.txt
* gs://folder/text2.pdf
* gs://folder/text3.tif
* Sentiment Analysis
* One or more CSV files where each line is a single column:
* GCS_FILE_PATH
* `GCS_FILE_PATH` is the Google Cloud Storage location of a text file.
* Supported file extensions: .TXT, .PDF, .TIF, .TIFF
* Text files can be no larger than 128kB in size.
* Sample rows:
* gs://folder/text1.txt
* gs://folder/text2.pdf
* gs://folder/text3.tif
* Entity Extraction
* One or more JSONL (JSON Lines) files that either provide inline text or
* documents. You can only use one format, either inline text or documents,
* for a single call to [AutoMl.BatchPredict].
* Each JSONL file contains a 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.
* Each document JSONL file contains, per line, a proto that wraps a Document
* proto with `input_config` set. Each document cannot exceed 2MB in size.
* Supported document extensions: .PDF, .TIF, .TIFF
* Each JSONL file must not exceed 100MB in size, and no more than 20
* JSONL files may be passed.
* Sample inline JSONL file (Shown with artificial line
* breaks. Actual line breaks are 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": "Extended sample content",
* "mime_type": "text/plain"
* }
* }
* Sample document JSONL file (Shown with artificial line
* breaks. Actual line breaks are 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.tif" ]
* }
* }
* }
* }
*
*
* AutoML Tables
* See [Preparing your training
* data](https://cloud.google.com/automl-tables/docs/predict-batch) for more
* information.
* You can use either
* [gcs_source][google.cloud.automl.v1.BatchPredictInputConfig.gcs_source]
* or
* [bigquery_source][BatchPredictInputConfig.bigquery_source].
* **For gcs_source:**
* 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.v1.TablesModelMetadata.input_feature_column_specs]
* [display_name-s][google.cloud.automl.v1.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.
* Sample rows from 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"}]}
*
* **For bigquery_source:**
* The 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.v1.TablesModelMetadata.input_feature_column_specs]
* [display_name-s][google.cloud.automl.v1.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.
*
*
* **Input field definitions:**
* `GCS_FILE_PATH`
* : The path to a file on Google Cloud Storage. For example,
* "gs://folder/video.avi".
* `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.
* **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.
*
* Generated from protobuf message google.cloud.automl.v1.BatchPredictInputConfig
*/
class BatchPredictInputConfig extends \Google\Protobuf\Internal\Message
{
protected $source;
/**
* Constructor.
*
* @param array $data {
* Optional. Data for populating the Message object.
*
* @type \Google\Cloud\AutoMl\V1\GcsSource $gcs_source
* Required. The Google Cloud Storage location for the input content.
* }
*/
public function __construct($data = NULL) {
\GPBMetadata\Google\Cloud\Automl\V1\Io::initOnce();
parent::__construct($data);
}
/**
* Required. The Google Cloud Storage location for the input content.
*
* Generated from protobuf field .google.cloud.automl.v1.GcsSource gcs_source = 1 [(.google.api.field_behavior) = REQUIRED];
* @return \Google\Cloud\AutoMl\V1\GcsSource|null
*/
public function getGcsSource()
{
return $this->readOneof(1);
}
public function hasGcsSource()
{
return $this->hasOneof(1);
}
/**
* Required. The Google Cloud Storage location for the input content.
*
* Generated from protobuf field .google.cloud.automl.v1.GcsSource gcs_source = 1 [(.google.api.field_behavior) = REQUIRED];
* @param \Google\Cloud\AutoMl\V1\GcsSource $var
* @return $this
*/
public function setGcsSource($var)
{
GPBUtil::checkMessage($var, \Google\Cloud\AutoMl\V1\GcsSource::class);
$this->writeOneof(1, $var);
return $this;
}
/**
* @return string
*/
public function getSource()
{
return $this->whichOneof("source");
}
}