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PROTO library for proto-google-cloud-automl-v1beta1
/*
* Copyright 2024 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.
*/
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: google/cloud/automl/v1beta1/prediction_service.proto
// Protobuf Java Version: 3.25.3
package com.google.cloud.automl.v1beta1;
/**
*
*
*
* Request message for [PredictionService.BatchPredict][google.cloud.automl.v1beta1.PredictionService.BatchPredict].
*
*
* Protobuf type {@code google.cloud.automl.v1beta1.BatchPredictRequest}
*/
public final class BatchPredictRequest extends com.google.protobuf.GeneratedMessageV3
implements
// @@protoc_insertion_point(message_implements:google.cloud.automl.v1beta1.BatchPredictRequest)
BatchPredictRequestOrBuilder {
private static final long serialVersionUID = 0L;
// Use BatchPredictRequest.newBuilder() to construct.
private BatchPredictRequest(com.google.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private BatchPredictRequest() {
name_ = "";
}
@java.lang.Override
@SuppressWarnings({"unused"})
protected java.lang.Object newInstance(UnusedPrivateParameter unused) {
return new BatchPredictRequest();
}
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
return com.google.cloud.automl.v1beta1.PredictionServiceProto
.internal_static_google_cloud_automl_v1beta1_BatchPredictRequest_descriptor;
}
@SuppressWarnings({"rawtypes"})
@java.lang.Override
protected com.google.protobuf.MapFieldReflectionAccessor internalGetMapFieldReflection(
int number) {
switch (number) {
case 5:
return internalGetParams();
default:
throw new RuntimeException("Invalid map field number: " + number);
}
}
@java.lang.Override
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return com.google.cloud.automl.v1beta1.PredictionServiceProto
.internal_static_google_cloud_automl_v1beta1_BatchPredictRequest_fieldAccessorTable
.ensureFieldAccessorsInitialized(
com.google.cloud.automl.v1beta1.BatchPredictRequest.class,
com.google.cloud.automl.v1beta1.BatchPredictRequest.Builder.class);
}
private int bitField0_;
public static final int NAME_FIELD_NUMBER = 1;
@SuppressWarnings("serial")
private volatile java.lang.Object name_ = "";
/**
*
*
*
* Required. Name of the model requested to serve the batch prediction.
*
*
*
* string name = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
*
*
* @return The name.
*/
@java.lang.Override
public java.lang.String getName() {
java.lang.Object ref = name_;
if (ref instanceof java.lang.String) {
return (java.lang.String) ref;
} else {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
name_ = s;
return s;
}
}
/**
*
*
*
* Required. Name of the model requested to serve the batch prediction.
*
*
*
* string name = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
*
*
* @return The bytes for name.
*/
@java.lang.Override
public com.google.protobuf.ByteString getNameBytes() {
java.lang.Object ref = name_;
if (ref instanceof java.lang.String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
name_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
public static final int INPUT_CONFIG_FIELD_NUMBER = 3;
private com.google.cloud.automl.v1beta1.BatchPredictInputConfig inputConfig_;
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*
* @return Whether the inputConfig field is set.
*/
@java.lang.Override
public boolean hasInputConfig() {
return ((bitField0_ & 0x00000001) != 0);
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*
* @return The inputConfig.
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.BatchPredictInputConfig getInputConfig() {
return inputConfig_ == null
? com.google.cloud.automl.v1beta1.BatchPredictInputConfig.getDefaultInstance()
: inputConfig_;
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.BatchPredictInputConfigOrBuilder
getInputConfigOrBuilder() {
return inputConfig_ == null
? com.google.cloud.automl.v1beta1.BatchPredictInputConfig.getDefaultInstance()
: inputConfig_;
}
public static final int OUTPUT_CONFIG_FIELD_NUMBER = 4;
private com.google.cloud.automl.v1beta1.BatchPredictOutputConfig outputConfig_;
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*
* @return Whether the outputConfig field is set.
*/
@java.lang.Override
public boolean hasOutputConfig() {
return ((bitField0_ & 0x00000002) != 0);
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*
* @return The outputConfig.
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.BatchPredictOutputConfig getOutputConfig() {
return outputConfig_ == null
? com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.getDefaultInstance()
: outputConfig_;
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
@java.lang.Override
public com.google.cloud.automl.v1beta1.BatchPredictOutputConfigOrBuilder
getOutputConfigOrBuilder() {
return outputConfig_ == null
? com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.getDefaultInstance()
: outputConfig_;
}
public static final int PARAMS_FIELD_NUMBER = 5;
private static final class ParamsDefaultEntryHolder {
static final com.google.protobuf.MapEntry defaultEntry =
com.google.protobuf.MapEntry.newDefaultInstance(
com.google.cloud.automl.v1beta1.PredictionServiceProto
.internal_static_google_cloud_automl_v1beta1_BatchPredictRequest_ParamsEntry_descriptor,
com.google.protobuf.WireFormat.FieldType.STRING,
"",
com.google.protobuf.WireFormat.FieldType.STRING,
"");
}
@SuppressWarnings("serial")
private com.google.protobuf.MapField params_;
private com.google.protobuf.MapField internalGetParams() {
if (params_ == null) {
return com.google.protobuf.MapField.emptyMapField(ParamsDefaultEntryHolder.defaultEntry);
}
return params_;
}
public int getParamsCount() {
return internalGetParams().getMap().size();
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
*
* * For Text Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
*
* * For Image Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
*
* * For Image Object Detection:
*
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
*
* * For Video Classification :
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
*
* * For Tables:
*
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
*
* * For Video Object Tracking:
*
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
@java.lang.Override
public boolean containsParams(java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
return internalGetParams().getMap().containsKey(key);
}
/** Use {@link #getParamsMap()} instead. */
@java.lang.Override
@java.lang.Deprecated
public java.util.Map getParams() {
return getParamsMap();
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
*
* * For Text Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
*
* * For Image Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
*
* * For Image Object Detection:
*
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
*
* * For Video Classification :
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
*
* * For Tables:
*
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
*
* * For Video Object Tracking:
*
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
@java.lang.Override
public java.util.Map getParamsMap() {
return internalGetParams().getMap();
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
*
* * For Text Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
*
* * For Image Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
*
* * For Image Object Detection:
*
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
*
* * For Video Classification :
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
*
* * For Tables:
*
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
*
* * For Video Object Tracking:
*
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
@java.lang.Override
public /* nullable */ java.lang.String getParamsOrDefault(
java.lang.String key,
/* nullable */
java.lang.String defaultValue) {
if (key == null) {
throw new NullPointerException("map key");
}
java.util.Map map = internalGetParams().getMap();
return map.containsKey(key) ? map.get(key) : defaultValue;
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
*
* * For Text Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
*
* * For Image Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
*
* * For Image Object Detection:
*
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
*
* * For Video Classification :
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
*
* * For Tables:
*
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
*
* * For Video Object Tracking:
*
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
@java.lang.Override
public java.lang.String getParamsOrThrow(java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
java.util.Map map = internalGetParams().getMap();
if (!map.containsKey(key)) {
throw new java.lang.IllegalArgumentException();
}
return map.get(key);
}
private byte memoizedIsInitialized = -1;
@java.lang.Override
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized == 1) return true;
if (isInitialized == 0) return false;
memoizedIsInitialized = 1;
return true;
}
@java.lang.Override
public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException {
if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(name_)) {
com.google.protobuf.GeneratedMessageV3.writeString(output, 1, name_);
}
if (((bitField0_ & 0x00000001) != 0)) {
output.writeMessage(3, getInputConfig());
}
if (((bitField0_ & 0x00000002) != 0)) {
output.writeMessage(4, getOutputConfig());
}
com.google.protobuf.GeneratedMessageV3.serializeStringMapTo(
output, internalGetParams(), ParamsDefaultEntryHolder.defaultEntry, 5);
getUnknownFields().writeTo(output);
}
@java.lang.Override
public int getSerializedSize() {
int size = memoizedSize;
if (size != -1) return size;
size = 0;
if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(name_)) {
size += com.google.protobuf.GeneratedMessageV3.computeStringSize(1, name_);
}
if (((bitField0_ & 0x00000001) != 0)) {
size += com.google.protobuf.CodedOutputStream.computeMessageSize(3, getInputConfig());
}
if (((bitField0_ & 0x00000002) != 0)) {
size += com.google.protobuf.CodedOutputStream.computeMessageSize(4, getOutputConfig());
}
for (java.util.Map.Entry entry :
internalGetParams().getMap().entrySet()) {
com.google.protobuf.MapEntry params__ =
ParamsDefaultEntryHolder.defaultEntry
.newBuilderForType()
.setKey(entry.getKey())
.setValue(entry.getValue())
.build();
size += com.google.protobuf.CodedOutputStream.computeMessageSize(5, params__);
}
size += getUnknownFields().getSerializedSize();
memoizedSize = size;
return size;
}
@java.lang.Override
public boolean equals(final java.lang.Object obj) {
if (obj == this) {
return true;
}
if (!(obj instanceof com.google.cloud.automl.v1beta1.BatchPredictRequest)) {
return super.equals(obj);
}
com.google.cloud.automl.v1beta1.BatchPredictRequest other =
(com.google.cloud.automl.v1beta1.BatchPredictRequest) obj;
if (!getName().equals(other.getName())) return false;
if (hasInputConfig() != other.hasInputConfig()) return false;
if (hasInputConfig()) {
if (!getInputConfig().equals(other.getInputConfig())) return false;
}
if (hasOutputConfig() != other.hasOutputConfig()) return false;
if (hasOutputConfig()) {
if (!getOutputConfig().equals(other.getOutputConfig())) return false;
}
if (!internalGetParams().equals(other.internalGetParams())) return false;
if (!getUnknownFields().equals(other.getUnknownFields())) return false;
return true;
}
@java.lang.Override
public int hashCode() {
if (memoizedHashCode != 0) {
return memoizedHashCode;
}
int hash = 41;
hash = (19 * hash) + getDescriptor().hashCode();
hash = (37 * hash) + NAME_FIELD_NUMBER;
hash = (53 * hash) + getName().hashCode();
if (hasInputConfig()) {
hash = (37 * hash) + INPUT_CONFIG_FIELD_NUMBER;
hash = (53 * hash) + getInputConfig().hashCode();
}
if (hasOutputConfig()) {
hash = (37 * hash) + OUTPUT_CONFIG_FIELD_NUMBER;
hash = (53 * hash) + getOutputConfig().hashCode();
}
if (!internalGetParams().getMap().isEmpty()) {
hash = (37 * hash) + PARAMS_FIELD_NUMBER;
hash = (53 * hash) + internalGetParams().hashCode();
}
hash = (29 * hash) + getUnknownFields().hashCode();
memoizedHashCode = hash;
return hash;
}
public static com.google.cloud.automl.v1beta1.BatchPredictRequest parseFrom(
java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static com.google.cloud.automl.v1beta1.BatchPredictRequest parseFrom(
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return PARSER.parseFrom(data);
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public static com.google.cloud.automl.v1beta1.BatchPredictRequest parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static com.google.cloud.automl.v1beta1.BatchPredictRequest parseFrom(byte[] data)
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public static com.google.cloud.automl.v1beta1.BatchPredictRequest parseFrom(
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public static com.google.cloud.automl.v1beta1.BatchPredictRequest parseFrom(
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public static com.google.cloud.automl.v1beta1.BatchPredictRequest parseFrom(
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return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
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public static com.google.cloud.automl.v1beta1.BatchPredictRequest parseDelimitedFrom(
java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
PARSER, input, extensionRegistry);
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public static com.google.cloud.automl.v1beta1.BatchPredictRequest parseFrom(
com.google.protobuf.CodedInputStream input) throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
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public static com.google.cloud.automl.v1beta1.BatchPredictRequest parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
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public Builder newBuilderForType() {
return newBuilder();
}
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
}
public static Builder newBuilder(com.google.cloud.automl.v1beta1.BatchPredictRequest prototype) {
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return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this);
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@java.lang.Override
protected Builder newBuilderForType(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
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/**
*
*
*
* Request message for [PredictionService.BatchPredict][google.cloud.automl.v1beta1.PredictionService.BatchPredict].
*
*
* Protobuf type {@code google.cloud.automl.v1beta1.BatchPredictRequest}
*/
public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder
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// @@protoc_insertion_point(builder_implements:google.cloud.automl.v1beta1.BatchPredictRequest)
com.google.cloud.automl.v1beta1.BatchPredictRequestOrBuilder {
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private Builder() {
maybeForceBuilderInitialization();
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private Builder(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
super(parent);
maybeForceBuilderInitialization();
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private void maybeForceBuilderInitialization() {
if (com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders) {
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getOutputConfigFieldBuilder();
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name_ = "";
inputConfig_ = null;
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outputConfigBuilder_ = null;
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internalGetMutableParams().clear();
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@java.lang.Override
public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() {
return com.google.cloud.automl.v1beta1.PredictionServiceProto
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@java.lang.Override
public com.google.cloud.automl.v1beta1.BatchPredictRequest getDefaultInstanceForType() {
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com.google.cloud.automl.v1beta1.BatchPredictRequest result = buildPartial();
if (!result.isInitialized()) {
throw newUninitializedMessageException(result);
}
return result;
}
@java.lang.Override
public com.google.cloud.automl.v1beta1.BatchPredictRequest buildPartial() {
com.google.cloud.automl.v1beta1.BatchPredictRequest result =
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if (bitField0_ != 0) {
buildPartial0(result);
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onBuilt();
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private void buildPartial0(com.google.cloud.automl.v1beta1.BatchPredictRequest result) {
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@java.lang.Override
public Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof) {
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@java.lang.Override
public Builder setRepeatedField(
com.google.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value) {
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@java.lang.Override
public Builder addRepeatedField(
com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) {
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if (other instanceof com.google.cloud.automl.v1beta1.BatchPredictRequest) {
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super.mergeFrom(other);
return this;
}
}
public Builder mergeFrom(com.google.cloud.automl.v1beta1.BatchPredictRequest other) {
if (other == com.google.cloud.automl.v1beta1.BatchPredictRequest.getDefaultInstance())
return this;
if (!other.getName().isEmpty()) {
name_ = other.name_;
bitField0_ |= 0x00000001;
onChanged();
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if (other.hasInputConfig()) {
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internalGetMutableParams().mergeFrom(other.internalGetParams());
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this.mergeUnknownFields(other.getUnknownFields());
onChanged();
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@java.lang.Override
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@java.lang.Override
public Builder mergeFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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case 10:
{
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bitField0_ |= 0x00000001;
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case 26:
{
input.readMessage(getInputConfigFieldBuilder().getBuilder(), extensionRegistry);
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case 34:
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input.readMessage(getOutputConfigFieldBuilder().getBuilder(), extensionRegistry);
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case 42:
{
com.google.protobuf.MapEntry params__ =
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ParamsDefaultEntryHolder.defaultEntry.getParserForType(),
extensionRegistry);
internalGetMutableParams()
.getMutableMap()
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bitField0_ |= 0x00000008;
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default:
{
if (!super.parseUnknownField(input, extensionRegistry, tag)) {
done = true; // was an endgroup tag
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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 name_ = "";
/**
*
*
*
* Required. Name of the model requested to serve the batch prediction.
*
*
*
* string name = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
*
*
* @return The name.
*/
public java.lang.String getName() {
java.lang.Object ref = name_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
name_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
*
*
* Required. Name of the model requested to serve the batch prediction.
*
*
*
* string name = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
*
*
* @return The bytes for name.
*/
public com.google.protobuf.ByteString getNameBytes() {
java.lang.Object ref = name_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
name_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
*
*
* Required. Name of the model requested to serve the batch prediction.
*
*
*
* string name = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
*
*
* @param value The name to set.
* @return This builder for chaining.
*/
public Builder setName(java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
name_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
/**
*
*
*
* Required. Name of the model requested to serve the batch prediction.
*
*
*
* string name = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
*
*
* @return This builder for chaining.
*/
public Builder clearName() {
name_ = getDefaultInstance().getName();
bitField0_ = (bitField0_ & ~0x00000001);
onChanged();
return this;
}
/**
*
*
*
* Required. Name of the model requested to serve the batch prediction.
*
*
*
* string name = 1 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = { ... }
*
*
* @param value The bytes for name to set.
* @return This builder for chaining.
*/
public Builder setNameBytes(com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
name_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
private com.google.cloud.automl.v1beta1.BatchPredictInputConfig inputConfig_;
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BatchPredictInputConfig,
com.google.cloud.automl.v1beta1.BatchPredictInputConfig.Builder,
com.google.cloud.automl.v1beta1.BatchPredictInputConfigOrBuilder>
inputConfigBuilder_;
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*
* @return Whether the inputConfig field is set.
*/
public boolean hasInputConfig() {
return ((bitField0_ & 0x00000002) != 0);
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*
* @return The inputConfig.
*/
public com.google.cloud.automl.v1beta1.BatchPredictInputConfig getInputConfig() {
if (inputConfigBuilder_ == null) {
return inputConfig_ == null
? com.google.cloud.automl.v1beta1.BatchPredictInputConfig.getDefaultInstance()
: inputConfig_;
} else {
return inputConfigBuilder_.getMessage();
}
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder setInputConfig(com.google.cloud.automl.v1beta1.BatchPredictInputConfig value) {
if (inputConfigBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
inputConfig_ = value;
} else {
inputConfigBuilder_.setMessage(value);
}
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder setInputConfig(
com.google.cloud.automl.v1beta1.BatchPredictInputConfig.Builder builderForValue) {
if (inputConfigBuilder_ == null) {
inputConfig_ = builderForValue.build();
} else {
inputConfigBuilder_.setMessage(builderForValue.build());
}
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder mergeInputConfig(com.google.cloud.automl.v1beta1.BatchPredictInputConfig value) {
if (inputConfigBuilder_ == null) {
if (((bitField0_ & 0x00000002) != 0)
&& inputConfig_ != null
&& inputConfig_
!= com.google.cloud.automl.v1beta1.BatchPredictInputConfig.getDefaultInstance()) {
getInputConfigBuilder().mergeFrom(value);
} else {
inputConfig_ = value;
}
} else {
inputConfigBuilder_.mergeFrom(value);
}
if (inputConfig_ != null) {
bitField0_ |= 0x00000002;
onChanged();
}
return this;
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder clearInputConfig() {
bitField0_ = (bitField0_ & ~0x00000002);
inputConfig_ = null;
if (inputConfigBuilder_ != null) {
inputConfigBuilder_.dispose();
inputConfigBuilder_ = null;
}
onChanged();
return this;
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
public com.google.cloud.automl.v1beta1.BatchPredictInputConfig.Builder getInputConfigBuilder() {
bitField0_ |= 0x00000002;
onChanged();
return getInputConfigFieldBuilder().getBuilder();
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
public com.google.cloud.automl.v1beta1.BatchPredictInputConfigOrBuilder
getInputConfigOrBuilder() {
if (inputConfigBuilder_ != null) {
return inputConfigBuilder_.getMessageOrBuilder();
} else {
return inputConfig_ == null
? com.google.cloud.automl.v1beta1.BatchPredictInputConfig.getDefaultInstance()
: inputConfig_;
}
}
/**
*
*
*
* Required. The input configuration for batch prediction.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictInputConfig input_config = 3 [(.google.api.field_behavior) = REQUIRED];
*
*/
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BatchPredictInputConfig,
com.google.cloud.automl.v1beta1.BatchPredictInputConfig.Builder,
com.google.cloud.automl.v1beta1.BatchPredictInputConfigOrBuilder>
getInputConfigFieldBuilder() {
if (inputConfigBuilder_ == null) {
inputConfigBuilder_ =
new com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BatchPredictInputConfig,
com.google.cloud.automl.v1beta1.BatchPredictInputConfig.Builder,
com.google.cloud.automl.v1beta1.BatchPredictInputConfigOrBuilder>(
getInputConfig(), getParentForChildren(), isClean());
inputConfig_ = null;
}
return inputConfigBuilder_;
}
private com.google.cloud.automl.v1beta1.BatchPredictOutputConfig outputConfig_;
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig,
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.Builder,
com.google.cloud.automl.v1beta1.BatchPredictOutputConfigOrBuilder>
outputConfigBuilder_;
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*
* @return Whether the outputConfig field is set.
*/
public boolean hasOutputConfig() {
return ((bitField0_ & 0x00000004) != 0);
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*
* @return The outputConfig.
*/
public com.google.cloud.automl.v1beta1.BatchPredictOutputConfig getOutputConfig() {
if (outputConfigBuilder_ == null) {
return outputConfig_ == null
? com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.getDefaultInstance()
: outputConfig_;
} else {
return outputConfigBuilder_.getMessage();
}
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder setOutputConfig(com.google.cloud.automl.v1beta1.BatchPredictOutputConfig value) {
if (outputConfigBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
outputConfig_ = value;
} else {
outputConfigBuilder_.setMessage(value);
}
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder setOutputConfig(
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.Builder builderForValue) {
if (outputConfigBuilder_ == null) {
outputConfig_ = builderForValue.build();
} else {
outputConfigBuilder_.setMessage(builderForValue.build());
}
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder mergeOutputConfig(
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig value) {
if (outputConfigBuilder_ == null) {
if (((bitField0_ & 0x00000004) != 0)
&& outputConfig_ != null
&& outputConfig_
!= com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.getDefaultInstance()) {
getOutputConfigBuilder().mergeFrom(value);
} else {
outputConfig_ = value;
}
} else {
outputConfigBuilder_.mergeFrom(value);
}
if (outputConfig_ != null) {
bitField0_ |= 0x00000004;
onChanged();
}
return this;
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
public Builder clearOutputConfig() {
bitField0_ = (bitField0_ & ~0x00000004);
outputConfig_ = null;
if (outputConfigBuilder_ != null) {
outputConfigBuilder_.dispose();
outputConfigBuilder_ = null;
}
onChanged();
return this;
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
public com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.Builder
getOutputConfigBuilder() {
bitField0_ |= 0x00000004;
onChanged();
return getOutputConfigFieldBuilder().getBuilder();
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
public com.google.cloud.automl.v1beta1.BatchPredictOutputConfigOrBuilder
getOutputConfigOrBuilder() {
if (outputConfigBuilder_ != null) {
return outputConfigBuilder_.getMessageOrBuilder();
} else {
return outputConfig_ == null
? com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.getDefaultInstance()
: outputConfig_;
}
}
/**
*
*
*
* Required. The Configuration specifying where output predictions should
* be written.
*
*
*
* .google.cloud.automl.v1beta1.BatchPredictOutputConfig output_config = 4 [(.google.api.field_behavior) = REQUIRED];
*
*/
private com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig,
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.Builder,
com.google.cloud.automl.v1beta1.BatchPredictOutputConfigOrBuilder>
getOutputConfigFieldBuilder() {
if (outputConfigBuilder_ == null) {
outputConfigBuilder_ =
new com.google.protobuf.SingleFieldBuilderV3<
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig,
com.google.cloud.automl.v1beta1.BatchPredictOutputConfig.Builder,
com.google.cloud.automl.v1beta1.BatchPredictOutputConfigOrBuilder>(
getOutputConfig(), getParentForChildren(), isClean());
outputConfig_ = null;
}
return outputConfigBuilder_;
}
private com.google.protobuf.MapField params_;
private com.google.protobuf.MapField internalGetParams() {
if (params_ == null) {
return com.google.protobuf.MapField.emptyMapField(ParamsDefaultEntryHolder.defaultEntry);
}
return params_;
}
private com.google.protobuf.MapField
internalGetMutableParams() {
if (params_ == null) {
params_ = com.google.protobuf.MapField.newMapField(ParamsDefaultEntryHolder.defaultEntry);
}
if (!params_.isMutable()) {
params_ = params_.copy();
}
bitField0_ |= 0x00000008;
onChanged();
return params_;
}
public int getParamsCount() {
return internalGetParams().getMap().size();
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
*
* * For Text Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
*
* * For Image Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
*
* * For Image Object Detection:
*
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
*
* * For Video Classification :
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
*
* * For Tables:
*
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
*
* * For Video Object Tracking:
*
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
@java.lang.Override
public boolean containsParams(java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
return internalGetParams().getMap().containsKey(key);
}
/** Use {@link #getParamsMap()} instead. */
@java.lang.Override
@java.lang.Deprecated
public java.util.Map getParams() {
return getParamsMap();
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
*
* * For Text Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
*
* * For Image Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
*
* * For Image Object Detection:
*
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
*
* * For Video Classification :
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
*
* * For Tables:
*
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
*
* * For Video Object Tracking:
*
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
@java.lang.Override
public java.util.Map getParamsMap() {
return internalGetParams().getMap();
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
*
* * For Text Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
*
* * For Image Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
*
* * For Image Object Detection:
*
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
*
* * For Video Classification :
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
*
* * For Tables:
*
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
*
* * For Video Object Tracking:
*
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
@java.lang.Override
public /* nullable */ java.lang.String getParamsOrDefault(
java.lang.String key,
/* nullable */
java.lang.String defaultValue) {
if (key == null) {
throw new NullPointerException("map key");
}
java.util.Map map = internalGetParams().getMap();
return map.containsKey(key) ? map.get(key) : defaultValue;
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
*
* * For Text Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
*
* * For Image Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
*
* * For Image Object Detection:
*
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
*
* * For Video Classification :
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
*
* * For Tables:
*
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
*
* * For Video Object Tracking:
*
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
@java.lang.Override
public java.lang.String getParamsOrThrow(java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
java.util.Map map = internalGetParams().getMap();
if (!map.containsKey(key)) {
throw new java.lang.IllegalArgumentException();
}
return map.get(key);
}
public Builder clearParams() {
bitField0_ = (bitField0_ & ~0x00000008);
internalGetMutableParams().getMutableMap().clear();
return this;
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
*
* * For Text Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
*
* * For Image Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
*
* * For Image Object Detection:
*
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
*
* * For Video Classification :
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
*
* * For Tables:
*
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
*
* * For Video Object Tracking:
*
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
public Builder removeParams(java.lang.String key) {
if (key == null) {
throw new NullPointerException("map key");
}
internalGetMutableParams().getMutableMap().remove(key);
return this;
}
/** Use alternate mutation accessors instead. */
@java.lang.Deprecated
public java.util.Map getMutableParams() {
bitField0_ |= 0x00000008;
return internalGetMutableParams().getMutableMap();
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
*
* * For Text Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
*
* * For Image Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
*
* * For Image Object Detection:
*
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
*
* * For Video Classification :
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
*
* * For Tables:
*
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
*
* * For Video Object Tracking:
*
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
public Builder putParams(java.lang.String key, java.lang.String value) {
if (key == null) {
throw new NullPointerException("map key");
}
if (value == null) {
throw new NullPointerException("map value");
}
internalGetMutableParams().getMutableMap().put(key, value);
bitField0_ |= 0x00000008;
return this;
}
/**
*
*
*
* Required. Additional domain-specific parameters for the predictions, any string must
* be up to 25000 characters long.
*
* * For Text Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a text snippet, it will only produce results
* that have at least this confidence score. The default is 0.5.
*
* * For Image Classification:
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for an image, it will only produce results that
* have at least this confidence score. The default is 0.5.
*
* * For Image Object Detection:
*
* `score_threshold` - (float) When Model detects objects on the image,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be produced per image. Default is 100, the
* requested value may be limited by server.
*
* * For Video Classification :
*
* `score_threshold` - (float) A value from 0.0 to 1.0. When the model
* makes predictions for a video, it will only produce results that
* have at least this confidence score. The default is 0.5.
* `segment_classification` - (boolean) Set to true to request
* segment-level classification. AutoML Video Intelligence returns
* labels and their confidence scores for the entire segment of the
* video that user specified in the request configuration.
* The default is "true".
* `shot_classification` - (boolean) Set to true to request shot-level
* classification. AutoML Video Intelligence determines the boundaries
* for each camera shot in the entire segment of the video that user
* specified in the request configuration. AutoML Video Intelligence
* then returns labels and their confidence scores for each detected
* shot, along with the start and end time of the shot.
* WARNING: Model evaluation is not done for this classification type,
* the quality of it depends on training data, but there are no metrics
* provided to describe that quality. The default is "false".
* `1s_interval_classification` - (boolean) Set to true to request
* classification for a video at one-second intervals. AutoML Video
* Intelligence returns labels and their confidence scores for each
* second of the entire segment of the video that user specified in the
* request configuration.
* WARNING: Model evaluation is not done for this classification
* type, the quality of it depends on training data, but there are no
* metrics provided to describe that quality. The default is
* "false".
*
* * For Tables:
*
* feature_imp<span>ortan</span>ce - (boolean) Whether feature importance
* should be populated in the returned TablesAnnotations. The
* default is false.
*
* * For Video Object Tracking:
*
* `score_threshold` - (float) When Model detects objects on video frames,
* it will only produce bounding boxes which have at least this
* confidence score. Value in 0 to 1 range, default is 0.5.
* `max_bounding_box_count` - (int64) No more than this number of bounding
* boxes will be returned per frame. Default is 100, the requested
* value may be limited by server.
* `min_bounding_box_size` - (float) Only bounding boxes with shortest edge
* at least that long as a relative value of video frame size will be
* returned. Value in 0 to 1 range. Default is 0.
*
*
* map<string, string> params = 5 [(.google.api.field_behavior) = REQUIRED];
*/
public Builder putAllParams(java.util.Map values) {
internalGetMutableParams().getMutableMap().putAll(values);
bitField0_ |= 0x00000008;
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.v1beta1.BatchPredictRequest)
}
// @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.BatchPredictRequest)
private static final com.google.cloud.automl.v1beta1.BatchPredictRequest DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new com.google.cloud.automl.v1beta1.BatchPredictRequest();
}
public static com.google.cloud.automl.v1beta1.BatchPredictRequest getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser PARSER =
new com.google.protobuf.AbstractParser() {
@java.lang.Override
public BatchPredictRequest 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.v1beta1.BatchPredictRequest getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}