Package com.google.cloud.automl.v1

Class ImageObjectDetectionModelMetadata.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderT>
com.google.protobuf.GeneratedMessage.Builder<ImageObjectDetectionModelMetadata.Builder>
com.google.cloud.automl.v1.ImageObjectDetectionModelMetadata.Builder
All Implemented Interfaces:
ImageObjectDetectionModelMetadataOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
Enclosing class:
ImageObjectDetectionModelMetadata
public static final class ImageObjectDetectionModelMetadata.Builder extends com.google.protobuf.GeneratedMessage.Builder<ImageObjectDetectionModelMetadata.Builder> implements ImageObjectDetectionModelMetadataOrBuilder
 Model metadata specific to image object detection.
Protobuf type google.cloud.automl.v1.ImageObjectDetectionModelMetadata

  • Method Details

    • getDescriptor

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

    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage.Builder<ImageObjectDetectionModelMetadata.Builder>

    • clear

      public ImageObjectDetectionModelMetadata.Builder clear()
      Specified by:
      clear in interface com.google.protobuf.Message.Builder
      Specified by:
      clear in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      clear in class com.google.protobuf.GeneratedMessage.Builder<ImageObjectDetectionModelMetadata.Builder>

    • getDescriptorForType

      public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
      Specified by:
      getDescriptorForType in interface com.google.protobuf.Message.Builder
      Specified by:
      getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
      Overrides:
      getDescriptorForType in class com.google.protobuf.GeneratedMessage.Builder<ImageObjectDetectionModelMetadata.Builder>

    • getDefaultInstanceForType

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

    • build

      public ImageObjectDetectionModelMetadata build()
      Specified by:
      build in interface com.google.protobuf.Message.Builder
      Specified by:
      build in interface com.google.protobuf.MessageLite.Builder

    • buildPartial

      public ImageObjectDetectionModelMetadata buildPartial()
      Specified by:
      buildPartial in interface com.google.protobuf.Message.Builder
      Specified by:
      buildPartial in interface com.google.protobuf.MessageLite.Builder

    • mergeFrom

      public ImageObjectDetectionModelMetadata.Builder mergeFrom(com.google.protobuf.Message other)
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<ImageObjectDetectionModelMetadata.Builder>

    • mergeFrom

      public ImageObjectDetectionModelMetadata.Builder mergeFrom(ImageObjectDetectionModelMetadata other)

    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
      Overrides:
      isInitialized in class com.google.protobuf.GeneratedMessage.Builder<ImageObjectDetectionModelMetadata.Builder>

    • mergeFrom

      public ImageObjectDetectionModelMetadata.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Specified by:
      mergeFrom in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<ImageObjectDetectionModelMetadata.Builder>
      Throws:
      IOException

    • getModelType

      public String getModelType()
       Optional. Type of the model. The available values are:
 *   `cloud-high-accuracy-1` - (default) A model to be used via prediction
 calls to AutoML API. Expected to have a higher latency, but
 should also have a higher prediction quality than other
 models.
 *   `cloud-low-latency-1` -  A model to be used via prediction
 calls to AutoML API. Expected to have low latency, but may
 have lower prediction quality than other models.
 *   `mobile-low-latency-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards. Expected to have low latency, but
 may have lower prediction quality than other models.
 *   `mobile-versatile-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards.
 *   `mobile-high-accuracy-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards.  Expected to have a higher
 latency, but should also have a higher prediction quality
 than other models.
      string model_type = 1 [(.google.api.field_behavior) = OPTIONAL];
      Specified by:
      getModelType in interface ImageObjectDetectionModelMetadataOrBuilder
      Returns:
      The modelType.

    • getModelTypeBytes

      public com.google.protobuf.ByteString getModelTypeBytes()
       Optional. Type of the model. The available values are:
 *   `cloud-high-accuracy-1` - (default) A model to be used via prediction
 calls to AutoML API. Expected to have a higher latency, but
 should also have a higher prediction quality than other
 models.
 *   `cloud-low-latency-1` -  A model to be used via prediction
 calls to AutoML API. Expected to have low latency, but may
 have lower prediction quality than other models.
 *   `mobile-low-latency-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards. Expected to have low latency, but
 may have lower prediction quality than other models.
 *   `mobile-versatile-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards.
 *   `mobile-high-accuracy-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards.  Expected to have a higher
 latency, but should also have a higher prediction quality
 than other models.
      string model_type = 1 [(.google.api.field_behavior) = OPTIONAL];
      Specified by:
      getModelTypeBytes in interface ImageObjectDetectionModelMetadataOrBuilder
      Returns:
      The bytes for modelType.

    • setModelType

      public ImageObjectDetectionModelMetadata.Builder setModelType(String value)
       Optional. Type of the model. The available values are:
 *   `cloud-high-accuracy-1` - (default) A model to be used via prediction
 calls to AutoML API. Expected to have a higher latency, but
 should also have a higher prediction quality than other
 models.
 *   `cloud-low-latency-1` -  A model to be used via prediction
 calls to AutoML API. Expected to have low latency, but may
 have lower prediction quality than other models.
 *   `mobile-low-latency-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards. Expected to have low latency, but
 may have lower prediction quality than other models.
 *   `mobile-versatile-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards.
 *   `mobile-high-accuracy-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards.  Expected to have a higher
 latency, but should also have a higher prediction quality
 than other models.
      string model_type = 1 [(.google.api.field_behavior) = OPTIONAL];
      Parameters:
      value - The modelType to set.
      Returns:
      This builder for chaining.

    • clearModelType

      public ImageObjectDetectionModelMetadata.Builder clearModelType()
       Optional. Type of the model. The available values are:
 *   `cloud-high-accuracy-1` - (default) A model to be used via prediction
 calls to AutoML API. Expected to have a higher latency, but
 should also have a higher prediction quality than other
 models.
 *   `cloud-low-latency-1` -  A model to be used via prediction
 calls to AutoML API. Expected to have low latency, but may
 have lower prediction quality than other models.
 *   `mobile-low-latency-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards. Expected to have low latency, but
 may have lower prediction quality than other models.
 *   `mobile-versatile-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards.
 *   `mobile-high-accuracy-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards.  Expected to have a higher
 latency, but should also have a higher prediction quality
 than other models.
      string model_type = 1 [(.google.api.field_behavior) = OPTIONAL];
      Returns:
      This builder for chaining.

    • setModelTypeBytes

      public ImageObjectDetectionModelMetadata.Builder setModelTypeBytes(com.google.protobuf.ByteString value)
       Optional. Type of the model. The available values are:
 *   `cloud-high-accuracy-1` - (default) A model to be used via prediction
 calls to AutoML API. Expected to have a higher latency, but
 should also have a higher prediction quality than other
 models.
 *   `cloud-low-latency-1` -  A model to be used via prediction
 calls to AutoML API. Expected to have low latency, but may
 have lower prediction quality than other models.
 *   `mobile-low-latency-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards. Expected to have low latency, but
 may have lower prediction quality than other models.
 *   `mobile-versatile-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards.
 *   `mobile-high-accuracy-1` - A model that, in addition to providing
 prediction via AutoML API, can also be exported (see
 [AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel]) and used on a mobile or edge device
 with TensorFlow afterwards.  Expected to have a higher
 latency, but should also have a higher prediction quality
 than other models.
      string model_type = 1 [(.google.api.field_behavior) = OPTIONAL];
      Parameters:
      value - The bytes for modelType to set.
      Returns:
      This builder for chaining.

    • getNodeCount

      public long getNodeCount()
       Output only. The number of nodes this model is deployed on. A node is an
 abstraction of a machine resource, which can handle online prediction QPS
 as given in the qps_per_node field.
      int64 node_count = 3 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getNodeCount in interface ImageObjectDetectionModelMetadataOrBuilder
      Returns:
      The nodeCount.

    • setNodeCount

      public ImageObjectDetectionModelMetadata.Builder setNodeCount(long value)
       Output only. The number of nodes this model is deployed on. A node is an
 abstraction of a machine resource, which can handle online prediction QPS
 as given in the qps_per_node field.
      int64 node_count = 3 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The nodeCount to set.
      Returns:
      This builder for chaining.

    • clearNodeCount

      public ImageObjectDetectionModelMetadata.Builder clearNodeCount()
       Output only. The number of nodes this model is deployed on. A node is an
 abstraction of a machine resource, which can handle online prediction QPS
 as given in the qps_per_node field.
      int64 node_count = 3 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      This builder for chaining.

    • getNodeQps

      public double getNodeQps()
       Output only. An approximate number of online prediction QPS that can
 be supported by this model per each node on which it is deployed.
      double node_qps = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getNodeQps in interface ImageObjectDetectionModelMetadataOrBuilder
      Returns:
      The nodeQps.

    • setNodeQps

      public ImageObjectDetectionModelMetadata.Builder setNodeQps(double value)
       Output only. An approximate number of online prediction QPS that can
 be supported by this model per each node on which it is deployed.
      double node_qps = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The nodeQps to set.
      Returns:
      This builder for chaining.

    • clearNodeQps

      public ImageObjectDetectionModelMetadata.Builder clearNodeQps()
       Output only. An approximate number of online prediction QPS that can
 be supported by this model per each node on which it is deployed.
      double node_qps = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      This builder for chaining.

    • getStopReason

      public String getStopReason()
       Output only. The reason that this create model operation stopped,
 e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
      string stop_reason = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getStopReason in interface ImageObjectDetectionModelMetadataOrBuilder
      Returns:
      The stopReason.

    • getStopReasonBytes

      public com.google.protobuf.ByteString getStopReasonBytes()
       Output only. The reason that this create model operation stopped,
 e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
      string stop_reason = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getStopReasonBytes in interface ImageObjectDetectionModelMetadataOrBuilder
      Returns:
      The bytes for stopReason.

    • setStopReason

      public ImageObjectDetectionModelMetadata.Builder setStopReason(String value)
       Output only. The reason that this create model operation stopped,
 e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
      string stop_reason = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The stopReason to set.
      Returns:
      This builder for chaining.

    • clearStopReason

      public ImageObjectDetectionModelMetadata.Builder clearStopReason()
       Output only. The reason that this create model operation stopped,
 e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
      string stop_reason = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      This builder for chaining.

    • setStopReasonBytes

      public ImageObjectDetectionModelMetadata.Builder setStopReasonBytes(com.google.protobuf.ByteString value)
       Output only. The reason that this create model operation stopped,
 e.g. `BUDGET_REACHED`, `MODEL_CONVERGED`.
      string stop_reason = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The bytes for stopReason to set.
      Returns:
      This builder for chaining.

    • getTrainBudgetMilliNodeHours

      public long getTrainBudgetMilliNodeHours()
       Optional. The train budget of creating this model, expressed in milli node
 hours i.e. 1,000 value in this field means 1 node hour. The actual
 `train_cost` will be equal or less than this value. If further model
 training ceases to provide any improvements, it will stop without using
 full budget and the stop_reason will be `MODEL_CONVERGED`.
 Note, node_hour  = actual_hour * number_of_nodes_invovled.
 For model type `cloud-high-accuracy-1`(default) and `cloud-low-latency-1`,
 the train budget must be between 20,000 and 900,000 milli node hours,
 inclusive. The default value is 216, 000 which represents one day in
 wall time.
 For model type `mobile-low-latency-1`, `mobile-versatile-1`,
 `mobile-high-accuracy-1`, `mobile-core-ml-low-latency-1`,
 `mobile-core-ml-versatile-1`, `mobile-core-ml-high-accuracy-1`, the train
 budget must be between 1,000 and 100,000 milli node hours, inclusive.
 The default value is 24, 000 which represents one day in wall time.
      int64 train_budget_milli_node_hours = 6 [(.google.api.field_behavior) = OPTIONAL];
      Specified by:
      getTrainBudgetMilliNodeHours in interface ImageObjectDetectionModelMetadataOrBuilder
      Returns:
      The trainBudgetMilliNodeHours.

    • setTrainBudgetMilliNodeHours

      public ImageObjectDetectionModelMetadata.Builder setTrainBudgetMilliNodeHours(long value)
       Optional. The train budget of creating this model, expressed in milli node
 hours i.e. 1,000 value in this field means 1 node hour. The actual
 `train_cost` will be equal or less than this value. If further model
 training ceases to provide any improvements, it will stop without using
 full budget and the stop_reason will be `MODEL_CONVERGED`.
 Note, node_hour  = actual_hour * number_of_nodes_invovled.
 For model type `cloud-high-accuracy-1`(default) and `cloud-low-latency-1`,
 the train budget must be between 20,000 and 900,000 milli node hours,
 inclusive. The default value is 216, 000 which represents one day in
 wall time.
 For model type `mobile-low-latency-1`, `mobile-versatile-1`,
 `mobile-high-accuracy-1`, `mobile-core-ml-low-latency-1`,
 `mobile-core-ml-versatile-1`, `mobile-core-ml-high-accuracy-1`, the train
 budget must be between 1,000 and 100,000 milli node hours, inclusive.
 The default value is 24, 000 which represents one day in wall time.
      int64 train_budget_milli_node_hours = 6 [(.google.api.field_behavior) = OPTIONAL];
      Parameters:
      value - The trainBudgetMilliNodeHours to set.
      Returns:
      This builder for chaining.

    • clearTrainBudgetMilliNodeHours

      public ImageObjectDetectionModelMetadata.Builder clearTrainBudgetMilliNodeHours()
       Optional. The train budget of creating this model, expressed in milli node
 hours i.e. 1,000 value in this field means 1 node hour. The actual
 `train_cost` will be equal or less than this value. If further model
 training ceases to provide any improvements, it will stop without using
 full budget and the stop_reason will be `MODEL_CONVERGED`.
 Note, node_hour  = actual_hour * number_of_nodes_invovled.
 For model type `cloud-high-accuracy-1`(default) and `cloud-low-latency-1`,
 the train budget must be between 20,000 and 900,000 milli node hours,
 inclusive. The default value is 216, 000 which represents one day in
 wall time.
 For model type `mobile-low-latency-1`, `mobile-versatile-1`,
 `mobile-high-accuracy-1`, `mobile-core-ml-low-latency-1`,
 `mobile-core-ml-versatile-1`, `mobile-core-ml-high-accuracy-1`, the train
 budget must be between 1,000 and 100,000 milli node hours, inclusive.
 The default value is 24, 000 which represents one day in wall time.
      int64 train_budget_milli_node_hours = 6 [(.google.api.field_behavior) = OPTIONAL];
      Returns:
      This builder for chaining.

    • getTrainCostMilliNodeHours

      public long getTrainCostMilliNodeHours()
       Output only. The actual train cost of creating this model, expressed in
 milli node hours, i.e. 1,000 value in this field means 1 node hour.
 Guaranteed to not exceed the train budget.
      int64 train_cost_milli_node_hours = 7 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Specified by:
      getTrainCostMilliNodeHours in interface ImageObjectDetectionModelMetadataOrBuilder
      Returns:
      The trainCostMilliNodeHours.

    • setTrainCostMilliNodeHours

      public ImageObjectDetectionModelMetadata.Builder setTrainCostMilliNodeHours(long value)
       Output only. The actual train cost of creating this model, expressed in
 milli node hours, i.e. 1,000 value in this field means 1 node hour.
 Guaranteed to not exceed the train budget.
      int64 train_cost_milli_node_hours = 7 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Parameters:
      value - The trainCostMilliNodeHours to set.
      Returns:
      This builder for chaining.

    • clearTrainCostMilliNodeHours

      public ImageObjectDetectionModelMetadata.Builder clearTrainCostMilliNodeHours()
       Output only. The actual train cost of creating this model, expressed in
 milli node hours, i.e. 1,000 value in this field means 1 node hour.
 Guaranteed to not exceed the train budget.
      int64 train_cost_milli_node_hours = 7 [(.google.api.field_behavior) = OUTPUT_ONLY];
      Returns:
      This builder for chaining.