implements
// @@protoc_insertion_point(builder_implements:google.cloud.automl.v1.ImageClassificationModelMetadata)
com.google.cloud.automl.v1.ImageClassificationModelMetadataOrBuilder {
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
return com.google.cloud.automl.v1.ImageProto
.internal_static_google_cloud_automl_v1_ImageClassificationModelMetadata_descriptor;
}
@java.lang.Override
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return com.google.cloud.automl.v1.ImageProto
.internal_static_google_cloud_automl_v1_ImageClassificationModelMetadata_fieldAccessorTable
.ensureFieldAccessorsInitialized(
com.google.cloud.automl.v1.ImageClassificationModelMetadata.class,
com.google.cloud.automl.v1.ImageClassificationModelMetadata.Builder.class);
}
// Construct using com.google.cloud.automl.v1.ImageClassificationModelMetadata.newBuilder()
private Builder() {}
private Builder(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
super(parent);
}
@java.lang.Override
public Builder clear() {
super.clear();
bitField0_ = 0;
baseModelId_ = "";
trainBudgetMilliNodeHours_ = 0L;
trainCostMilliNodeHours_ = 0L;
stopReason_ = "";
modelType_ = "";
nodeQps_ = 0D;
nodeCount_ = 0L;
return this;
}
@java.lang.Override
public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() {
return com.google.cloud.automl.v1.ImageProto
.internal_static_google_cloud_automl_v1_ImageClassificationModelMetadata_descriptor;
}
@java.lang.Override
public com.google.cloud.automl.v1.ImageClassificationModelMetadata getDefaultInstanceForType() {
return com.google.cloud.automl.v1.ImageClassificationModelMetadata.getDefaultInstance();
}
@java.lang.Override
public com.google.cloud.automl.v1.ImageClassificationModelMetadata build() {
com.google.cloud.automl.v1.ImageClassificationModelMetadata result = buildPartial();
if (!result.isInitialized()) {
throw newUninitializedMessageException(result);
}
return result;
}
@java.lang.Override
public com.google.cloud.automl.v1.ImageClassificationModelMetadata buildPartial() {
com.google.cloud.automl.v1.ImageClassificationModelMetadata result =
new com.google.cloud.automl.v1.ImageClassificationModelMetadata(this);
if (bitField0_ != 0) {
buildPartial0(result);
}
onBuilt();
return result;
}
private void buildPartial0(com.google.cloud.automl.v1.ImageClassificationModelMetadata result) {
int from_bitField0_ = bitField0_;
if (((from_bitField0_ & 0x00000001) != 0)) {
result.baseModelId_ = baseModelId_;
}
if (((from_bitField0_ & 0x00000002) != 0)) {
result.trainBudgetMilliNodeHours_ = trainBudgetMilliNodeHours_;
}
if (((from_bitField0_ & 0x00000004) != 0)) {
result.trainCostMilliNodeHours_ = trainCostMilliNodeHours_;
}
if (((from_bitField0_ & 0x00000008) != 0)) {
result.stopReason_ = stopReason_;
}
if (((from_bitField0_ & 0x00000010) != 0)) {
result.modelType_ = modelType_;
}
if (((from_bitField0_ & 0x00000020) != 0)) {
result.nodeQps_ = nodeQps_;
}
if (((from_bitField0_ & 0x00000040) != 0)) {
result.nodeCount_ = nodeCount_;
}
}
@java.lang.Override
public Builder clone() {
return super.clone();
}
@java.lang.Override
public Builder setField(
com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) {
return super.setField(field, value);
}
@java.lang.Override
public Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field) {
return super.clearField(field);
}
@java.lang.Override
public Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof) {
return super.clearOneof(oneof);
}
@java.lang.Override
public Builder setRepeatedField(
com.google.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value) {
return super.setRepeatedField(field, index, value);
}
@java.lang.Override
public Builder addRepeatedField(
com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) {
return super.addRepeatedField(field, value);
}
@java.lang.Override
public Builder mergeFrom(com.google.protobuf.Message other) {
if (other instanceof com.google.cloud.automl.v1.ImageClassificationModelMetadata) {
return mergeFrom((com.google.cloud.automl.v1.ImageClassificationModelMetadata) other);
} else {
super.mergeFrom(other);
return this;
}
}
public Builder mergeFrom(com.google.cloud.automl.v1.ImageClassificationModelMetadata other) {
if (other == com.google.cloud.automl.v1.ImageClassificationModelMetadata.getDefaultInstance())
return this;
if (!other.getBaseModelId().isEmpty()) {
baseModelId_ = other.baseModelId_;
bitField0_ |= 0x00000001;
onChanged();
}
if (other.getTrainBudgetMilliNodeHours() != 0L) {
setTrainBudgetMilliNodeHours(other.getTrainBudgetMilliNodeHours());
}
if (other.getTrainCostMilliNodeHours() != 0L) {
setTrainCostMilliNodeHours(other.getTrainCostMilliNodeHours());
}
if (!other.getStopReason().isEmpty()) {
stopReason_ = other.stopReason_;
bitField0_ |= 0x00000008;
onChanged();
}
if (!other.getModelType().isEmpty()) {
modelType_ = other.modelType_;
bitField0_ |= 0x00000010;
onChanged();
}
if (other.getNodeQps() != 0D) {
setNodeQps(other.getNodeQps());
}
if (other.getNodeCount() != 0L) {
setNodeCount(other.getNodeCount());
}
this.mergeUnknownFields(other.getUnknownFields());
onChanged();
return this;
}
@java.lang.Override
public final boolean isInitialized() {
return true;
}
@java.lang.Override
public Builder mergeFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
if (extensionRegistry == null) {
throw new java.lang.NullPointerException();
}
try {
boolean done = false;
while (!done) {
int tag = input.readTag();
switch (tag) {
case 0:
done = true;
break;
case 10:
{
baseModelId_ = input.readStringRequireUtf8();
bitField0_ |= 0x00000001;
break;
} // case 10
case 42:
{
stopReason_ = input.readStringRequireUtf8();
bitField0_ |= 0x00000008;
break;
} // case 42
case 58:
{
modelType_ = input.readStringRequireUtf8();
bitField0_ |= 0x00000010;
break;
} // case 58
case 105:
{
nodeQps_ = input.readDouble();
bitField0_ |= 0x00000020;
break;
} // case 105
case 112:
{
nodeCount_ = input.readInt64();
bitField0_ |= 0x00000040;
break;
} // case 112
case 128:
{
trainBudgetMilliNodeHours_ = input.readInt64();
bitField0_ |= 0x00000002;
break;
} // case 128
case 136:
{
trainCostMilliNodeHours_ = input.readInt64();
bitField0_ |= 0x00000004;
break;
} // case 136
default:
{
if (!super.parseUnknownField(input, extensionRegistry, tag)) {
done = true; // was an endgroup tag
}
break;
} // default:
} // switch (tag)
} // while (!done)
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.unwrapIOException();
} finally {
onChanged();
} // finally
return this;
}
private int bitField0_;
private java.lang.Object baseModelId_ = "";
/**
*
*
*
* Optional. The ID of the `base` model. If it is specified, the new model
* will be created based on the `base` model. Otherwise, the new model will be
* created from scratch. The `base` model must be in the same
* `project` and `location` as the new model to create, and have the same
* `model_type`.
*
*
* string base_model_id = 1 [(.google.api.field_behavior) = OPTIONAL];
*
* @return The baseModelId.
*/
public java.lang.String getBaseModelId() {
java.lang.Object ref = baseModelId_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
baseModelId_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
*
*
* Optional. The ID of the `base` model. If it is specified, the new model
* will be created based on the `base` model. Otherwise, the new model will be
* created from scratch. The `base` model must be in the same
* `project` and `location` as the new model to create, and have the same
* `model_type`.
*
*
* string base_model_id = 1 [(.google.api.field_behavior) = OPTIONAL];
*
* @return The bytes for baseModelId.
*/
public com.google.protobuf.ByteString getBaseModelIdBytes() {
java.lang.Object ref = baseModelId_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
baseModelId_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
*
*
* Optional. The ID of the `base` model. If it is specified, the new model
* will be created based on the `base` model. Otherwise, the new model will be
* created from scratch. The `base` model must be in the same
* `project` and `location` as the new model to create, and have the same
* `model_type`.
*
*
* string base_model_id = 1 [(.google.api.field_behavior) = OPTIONAL];
*
* @param value The baseModelId to set.
* @return This builder for chaining.
*/
public Builder setBaseModelId(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
baseModelId_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
/**
*
*
*
* Optional. The ID of the `base` model. If it is specified, the new model
* will be created based on the `base` model. Otherwise, the new model will be
* created from scratch. The `base` model must be in the same
* `project` and `location` as the new model to create, and have the same
* `model_type`.
*
*
* string base_model_id = 1 [(.google.api.field_behavior) = OPTIONAL];
*
* @return This builder for chaining.
*/
public Builder clearBaseModelId() {
baseModelId_ = getDefaultInstance().getBaseModelId();
bitField0_ = (bitField0_ & ~0x00000001);
onChanged();
return this;
}
/**
*
*
*
* Optional. The ID of the `base` model. If it is specified, the new model
* will be created based on the `base` model. Otherwise, the new model will be
* created from scratch. The `base` model must be in the same
* `project` and `location` as the new model to create, and have the same
* `model_type`.
*
*
* string base_model_id = 1 [(.google.api.field_behavior) = OPTIONAL];
*
* @param value The bytes for baseModelId to set.
* @return This builder for chaining.
*/
public Builder setBaseModelIdBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
baseModelId_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
private long trainBudgetMilliNodeHours_;
/**
*
*
*
* 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`(default), the train budget must be between 8,000
* and 800,000 milli node hours, inclusive. The default value is 192, 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 = 16 [(.google.api.field_behavior) = OPTIONAL];
*
*
* @return The trainBudgetMilliNodeHours.
*/
@java.lang.Override
public long getTrainBudgetMilliNodeHours() {
return trainBudgetMilliNodeHours_;
}
/**
*
*
*
* 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`(default), the train budget must be between 8,000
* and 800,000 milli node hours, inclusive. The default value is 192, 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 = 16 [(.google.api.field_behavior) = OPTIONAL];
*
*
* @param value The trainBudgetMilliNodeHours to set.
* @return This builder for chaining.
*/
public Builder setTrainBudgetMilliNodeHours(long value) {
trainBudgetMilliNodeHours_ = value;
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* 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`(default), the train budget must be between 8,000
* and 800,000 milli node hours, inclusive. The default value is 192, 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 = 16 [(.google.api.field_behavior) = OPTIONAL];
*
*
* @return This builder for chaining.
*/
public Builder clearTrainBudgetMilliNodeHours() {
bitField0_ = (bitField0_ & ~0x00000002);
trainBudgetMilliNodeHours_ = 0L;
onChanged();
return this;
}
private long trainCostMilliNodeHours_;
/**
*
*
*
* 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 = 17 [(.google.api.field_behavior) = OUTPUT_ONLY];
*
*
* @return The trainCostMilliNodeHours.
*/
@java.lang.Override
public long getTrainCostMilliNodeHours() {
return trainCostMilliNodeHours_;
}
/**
*
*
*
* 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 = 17 [(.google.api.field_behavior) = OUTPUT_ONLY];
*
*
* @param value The trainCostMilliNodeHours to set.
* @return This builder for chaining.
*/
public Builder setTrainCostMilliNodeHours(long value) {
trainCostMilliNodeHours_ = value;
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
*
*
* 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 = 17 [(.google.api.field_behavior) = OUTPUT_ONLY];
*
*
* @return This builder for chaining.
*/
public Builder clearTrainCostMilliNodeHours() {
bitField0_ = (bitField0_ & ~0x00000004);
trainCostMilliNodeHours_ = 0L;
onChanged();
return this;
}
private java.lang.Object stopReason_ = "";
/**
*
*
*
* 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];
*
* @return The stopReason.
*/
public java.lang.String getStopReason() {
java.lang.Object ref = stopReason_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
stopReason_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
*
*
* 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];
*
* @return The bytes for stopReason.
*/
public com.google.protobuf.ByteString getStopReasonBytes() {
java.lang.Object ref = stopReason_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
stopReason_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
*
*
* 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];
*
* @param value The stopReason to set.
* @return This builder for chaining.
*/
public Builder setStopReason(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
stopReason_ = value;
bitField0_ |= 0x00000008;
onChanged();
return this;
}
/**
*
*
*
* 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];
*
* @return This builder for chaining.
*/
public Builder clearStopReason() {
stopReason_ = getDefaultInstance().getStopReason();
bitField0_ = (bitField0_ & ~0x00000008);
onChanged();
return this;
}
/**
*
*
*
* 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];
*
* @param value The bytes for stopReason to set.
* @return This builder for chaining.
*/
public Builder setStopReasonBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
stopReason_ = value;
bitField0_ |= 0x00000008;
onChanged();
return this;
}
private java.lang.Object modelType_ = "";
/**
*
*
*
* Optional. Type of the model. The available values are:
* * `cloud` - Model to be used via prediction calls to AutoML API.
* This is the default value.
* * `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.
* * `mobile-core-ml-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 device with Core
* ML afterwards. Expected to have low latency, but may have
* lower prediction quality than other models.
* * `mobile-core-ml-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 device with Core
* ML afterwards.
* * `mobile-core-ml-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 device with
* Core ML afterwards. Expected to have a higher latency, but
* should also have a higher prediction quality than other
* models.
*
*
* string model_type = 7 [(.google.api.field_behavior) = OPTIONAL];
*
* @return The modelType.
*/
public java.lang.String getModelType() {
java.lang.Object ref = modelType_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
modelType_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
*
*
* Optional. Type of the model. The available values are:
* * `cloud` - Model to be used via prediction calls to AutoML API.
* This is the default value.
* * `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.
* * `mobile-core-ml-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 device with Core
* ML afterwards. Expected to have low latency, but may have
* lower prediction quality than other models.
* * `mobile-core-ml-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 device with Core
* ML afterwards.
* * `mobile-core-ml-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 device with
* Core ML afterwards. Expected to have a higher latency, but
* should also have a higher prediction quality than other
* models.
*
*
* string model_type = 7 [(.google.api.field_behavior) = OPTIONAL];
*
* @return The bytes for modelType.
*/
public com.google.protobuf.ByteString getModelTypeBytes() {
java.lang.Object ref = modelType_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
modelType_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
*
*
* Optional. Type of the model. The available values are:
* * `cloud` - Model to be used via prediction calls to AutoML API.
* This is the default value.
* * `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.
* * `mobile-core-ml-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 device with Core
* ML afterwards. Expected to have low latency, but may have
* lower prediction quality than other models.
* * `mobile-core-ml-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 device with Core
* ML afterwards.
* * `mobile-core-ml-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 device with
* Core ML afterwards. Expected to have a higher latency, but
* should also have a higher prediction quality than other
* models.
*
*
* string model_type = 7 [(.google.api.field_behavior) = OPTIONAL];
*
* @param value The modelType to set.
* @return This builder for chaining.
*/
public Builder setModelType(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
modelType_ = value;
bitField0_ |= 0x00000010;
onChanged();
return this;
}
/**
*
*
*
* Optional. Type of the model. The available values are:
* * `cloud` - Model to be used via prediction calls to AutoML API.
* This is the default value.
* * `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.
* * `mobile-core-ml-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 device with Core
* ML afterwards. Expected to have low latency, but may have
* lower prediction quality than other models.
* * `mobile-core-ml-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 device with Core
* ML afterwards.
* * `mobile-core-ml-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 device with
* Core ML afterwards. Expected to have a higher latency, but
* should also have a higher prediction quality than other
* models.
*
*
* string model_type = 7 [(.google.api.field_behavior) = OPTIONAL];
*
* @return This builder for chaining.
*/
public Builder clearModelType() {
modelType_ = getDefaultInstance().getModelType();
bitField0_ = (bitField0_ & ~0x00000010);
onChanged();
return this;
}
/**
*
*
*
* Optional. Type of the model. The available values are:
* * `cloud` - Model to be used via prediction calls to AutoML API.
* This is the default value.
* * `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.
* * `mobile-core-ml-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 device with Core
* ML afterwards. Expected to have low latency, but may have
* lower prediction quality than other models.
* * `mobile-core-ml-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 device with Core
* ML afterwards.
* * `mobile-core-ml-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 device with
* Core ML afterwards. Expected to have a higher latency, but
* should also have a higher prediction quality than other
* models.
*
*
* string model_type = 7 [(.google.api.field_behavior) = OPTIONAL];
*
* @param value The bytes for modelType to set.
* @return This builder for chaining.
*/
public Builder setModelTypeBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
modelType_ = value;
bitField0_ |= 0x00000010;
onChanged();
return this;
}
private double nodeQps_;
/**
*
*
*
* 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 = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
*
* @return The nodeQps.
*/
@java.lang.Override
public double getNodeQps() {
return nodeQps_;
}
/**
*
*
*
* 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 = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
*
* @param value The nodeQps to set.
* @return This builder for chaining.
*/
public Builder setNodeQps(double value) {
nodeQps_ = value;
bitField0_ |= 0x00000020;
onChanged();
return this;
}
/**
*
*
*
* 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 = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];
*
* @return This builder for chaining.
*/
public Builder clearNodeQps() {
bitField0_ = (bitField0_ & ~0x00000020);
nodeQps_ = 0D;
onChanged();
return this;
}
private long nodeCount_;
/**
*
*
*
* 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 node_qps field.
*
*
* int64 node_count = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];
*
* @return The nodeCount.
*/
@java.lang.Override
public long getNodeCount() {
return nodeCount_;
}
/**
*
*
*
* 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 node_qps field.
*
*
* int64 node_count = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];
*
* @param value The nodeCount to set.
* @return This builder for chaining.
*/
public Builder setNodeCount(long value) {
nodeCount_ = value;
bitField0_ |= 0x00000040;
onChanged();
return this;
}
/**
*
*
*
* 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 node_qps field.
*
*
* int64 node_count = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];
*
* @return This builder for chaining.
*/
public Builder clearNodeCount() {
bitField0_ = (bitField0_ & ~0x00000040);
nodeCount_ = 0L;
onChanged();
return this;
}
@java.lang.Override
public final Builder setUnknownFields(final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:google.cloud.automl.v1.ImageClassificationModelMetadata)
}
// @@protoc_insertion_point(class_scope:google.cloud.automl.v1.ImageClassificationModelMetadata)
private static final com.google.cloud.automl.v1.ImageClassificationModelMetadata DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new com.google.cloud.automl.v1.ImageClassificationModelMetadata();
}
public static com.google.cloud.automl.v1.ImageClassificationModelMetadata getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser PARSER =
new com.google.protobuf.AbstractParser() {
@java.lang.Override
public ImageClassificationModelMetadata parsePartialFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
Builder builder = newBuilder();
try {
builder.mergeFrom(input, extensionRegistry);
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.setUnfinishedMessage(builder.buildPartial());
} catch (com.google.protobuf.UninitializedMessageException e) {
throw e.asInvalidProtocolBufferException().setUnfinishedMessage(builder.buildPartial());
} catch (java.io.IOException e) {
throw new com.google.protobuf.InvalidProtocolBufferException(e)
.setUnfinishedMessage(builder.buildPartial());
}
return builder.buildPartial();
}
};
public static com.google.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public com.google.cloud.automl.v1.ImageClassificationModelMetadata getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}