org.tensorflow.framework.RewriterConfig Maven / Gradle / Ivy
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/core/protobuf/rewriter_config.proto
package org.tensorflow.framework;
/**
*
* Graph rewriting is experimental and subject to change, not covered by any
* API stability guarantees.
*
*
* Protobuf type {@code tensorflow.RewriterConfig}
*/
public final class RewriterConfig extends
org.nd4j.shade.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.RewriterConfig)
RewriterConfigOrBuilder {
private static final long serialVersionUID = 0L;
// Use RewriterConfig.newBuilder() to construct.
private RewriterConfig(org.nd4j.shade.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private RewriterConfig() {
layoutOptimizer_ = 0;
constantFolding_ = 0;
shapeOptimization_ = 0;
remapping_ = 0;
arithmeticOptimization_ = 0;
dependencyOptimization_ = 0;
loopOptimization_ = 0;
functionOptimization_ = 0;
debugStripper_ = 0;
scopedAllocatorOptimization_ = 0;
metaOptimizerIterations_ = 0;
memoryOptimization_ = 0;
memoryOptimizerTargetNodeNameScope_ = "";
optimizers_ = org.nd4j.shade.protobuf.LazyStringArrayList.EMPTY;
customOptimizers_ = java.util.Collections.emptyList();
}
@java.lang.Override
@SuppressWarnings({"unused"})
protected java.lang.Object newInstance(
UnusedPrivateParameter unused) {
return new RewriterConfig();
}
@java.lang.Override
public final org.nd4j.shade.protobuf.UnknownFieldSet
getUnknownFields() {
return this.unknownFields;
}
private RewriterConfig(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
this();
if (extensionRegistry == null) {
throw new java.lang.NullPointerException();
}
int mutable_bitField0_ = 0;
org.nd4j.shade.protobuf.UnknownFieldSet.Builder unknownFields =
org.nd4j.shade.protobuf.UnknownFieldSet.newBuilder();
try {
boolean done = false;
while (!done) {
int tag = input.readTag();
switch (tag) {
case 0:
done = true;
break;
case 8: {
int rawValue = input.readEnum();
layoutOptimizer_ = rawValue;
break;
}
case 16: {
disableModelPruning_ = input.readBool();
break;
}
case 24: {
int rawValue = input.readEnum();
constantFolding_ = rawValue;
break;
}
case 32: {
int rawValue = input.readEnum();
memoryOptimization_ = rawValue;
break;
}
case 42: {
org.tensorflow.framework.AutoParallelOptions.Builder subBuilder = null;
if (autoParallel_ != null) {
subBuilder = autoParallel_.toBuilder();
}
autoParallel_ = input.readMessage(org.tensorflow.framework.AutoParallelOptions.parser(), extensionRegistry);
if (subBuilder != null) {
subBuilder.mergeFrom(autoParallel_);
autoParallel_ = subBuilder.buildPartial();
}
break;
}
case 50: {
java.lang.String s = input.readStringRequireUtf8();
memoryOptimizerTargetNodeNameScope_ = s;
break;
}
case 56: {
int rawValue = input.readEnum();
arithmeticOptimization_ = rawValue;
break;
}
case 64: {
int rawValue = input.readEnum();
dependencyOptimization_ = rawValue;
break;
}
case 72: {
int rawValue = input.readEnum();
loopOptimization_ = rawValue;
break;
}
case 80: {
int rawValue = input.readEnum();
functionOptimization_ = rawValue;
break;
}
case 88: {
int rawValue = input.readEnum();
debugStripper_ = rawValue;
break;
}
case 96: {
int rawValue = input.readEnum();
metaOptimizerIterations_ = rawValue;
break;
}
case 104: {
int rawValue = input.readEnum();
shapeOptimization_ = rawValue;
break;
}
case 112: {
int rawValue = input.readEnum();
remapping_ = rawValue;
break;
}
case 120: {
int rawValue = input.readEnum();
scopedAllocatorOptimization_ = rawValue;
break;
}
case 130: {
org.tensorflow.framework.ScopedAllocatorOptions.Builder subBuilder = null;
if (scopedAllocatorOpts_ != null) {
subBuilder = scopedAllocatorOpts_.toBuilder();
}
scopedAllocatorOpts_ = input.readMessage(org.tensorflow.framework.ScopedAllocatorOptions.parser(), extensionRegistry);
if (subBuilder != null) {
subBuilder.mergeFrom(scopedAllocatorOpts_);
scopedAllocatorOpts_ = subBuilder.buildPartial();
}
break;
}
case 802: {
java.lang.String s = input.readStringRequireUtf8();
if (!((mutable_bitField0_ & 0x00000001) != 0)) {
optimizers_ = new org.nd4j.shade.protobuf.LazyStringArrayList();
mutable_bitField0_ |= 0x00000001;
}
optimizers_.add(s);
break;
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case 1602: {
if (!((mutable_bitField0_ & 0x00000002) != 0)) {
customOptimizers_ = new java.util.ArrayList();
mutable_bitField0_ |= 0x00000002;
}
customOptimizers_.add(
input.readMessage(org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.parser(), extensionRegistry));
break;
}
default: {
if (!parseUnknownField(
input, unknownFields, extensionRegistry, tag)) {
done = true;
}
break;
}
}
}
} catch (org.nd4j.shade.protobuf.InvalidProtocolBufferException e) {
throw e.setUnfinishedMessage(this);
} catch (java.io.IOException e) {
throw new org.nd4j.shade.protobuf.InvalidProtocolBufferException(
e).setUnfinishedMessage(this);
} finally {
if (((mutable_bitField0_ & 0x00000001) != 0)) {
optimizers_ = optimizers_.getUnmodifiableView();
}
if (((mutable_bitField0_ & 0x00000002) != 0)) {
customOptimizers_ = java.util.Collections.unmodifiableList(customOptimizers_);
}
this.unknownFields = unknownFields.build();
makeExtensionsImmutable();
}
}
public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_descriptor;
}
@java.lang.Override
protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_fieldAccessorTable
.ensureFieldAccessorsInitialized(
org.tensorflow.framework.RewriterConfig.class, org.tensorflow.framework.RewriterConfig.Builder.class);
}
/**
* Protobuf enum {@code tensorflow.RewriterConfig.Toggle}
*/
public enum Toggle
implements org.nd4j.shade.protobuf.ProtocolMessageEnum {
/**
* DEFAULT = 0;
*/
DEFAULT(0),
/**
* ON = 1;
*/
ON(1),
/**
* OFF = 2;
*/
OFF(2),
/**
*
* Enable some aggressive optimizations that use assumptions that TF graphs
* may break. For example, assume the shape of a placeholder matches its
* actual feed.
*
*
* AGGRESSIVE = 3;
*/
AGGRESSIVE(3),
UNRECOGNIZED(-1),
;
/**
* DEFAULT = 0;
*/
public static final int DEFAULT_VALUE = 0;
/**
* ON = 1;
*/
public static final int ON_VALUE = 1;
/**
* OFF = 2;
*/
public static final int OFF_VALUE = 2;
/**
*
* Enable some aggressive optimizations that use assumptions that TF graphs
* may break. For example, assume the shape of a placeholder matches its
* actual feed.
*
*
* AGGRESSIVE = 3;
*/
public static final int AGGRESSIVE_VALUE = 3;
public final int getNumber() {
if (this == UNRECOGNIZED) {
throw new java.lang.IllegalArgumentException(
"Can't get the number of an unknown enum value.");
}
return value;
}
/**
* @deprecated Use {@link #forNumber(int)} instead.
*/
@java.lang.Deprecated
public static Toggle valueOf(int value) {
return forNumber(value);
}
public static Toggle forNumber(int value) {
switch (value) {
case 0: return DEFAULT;
case 1: return ON;
case 2: return OFF;
case 3: return AGGRESSIVE;
default: return null;
}
}
public static org.nd4j.shade.protobuf.Internal.EnumLiteMap
internalGetValueMap() {
return internalValueMap;
}
private static final org.nd4j.shade.protobuf.Internal.EnumLiteMap<
Toggle> internalValueMap =
new org.nd4j.shade.protobuf.Internal.EnumLiteMap() {
public Toggle findValueByNumber(int number) {
return Toggle.forNumber(number);
}
};
public final org.nd4j.shade.protobuf.Descriptors.EnumValueDescriptor
getValueDescriptor() {
return getDescriptor().getValues().get(ordinal());
}
public final org.nd4j.shade.protobuf.Descriptors.EnumDescriptor
getDescriptorForType() {
return getDescriptor();
}
public static final org.nd4j.shade.protobuf.Descriptors.EnumDescriptor
getDescriptor() {
return org.tensorflow.framework.RewriterConfig.getDescriptor().getEnumTypes().get(0);
}
private static final Toggle[] VALUES = values();
public static Toggle valueOf(
org.nd4j.shade.protobuf.Descriptors.EnumValueDescriptor desc) {
if (desc.getType() != getDescriptor()) {
throw new java.lang.IllegalArgumentException(
"EnumValueDescriptor is not for this type.");
}
if (desc.getIndex() == -1) {
return UNRECOGNIZED;
}
return VALUES[desc.getIndex()];
}
private final int value;
private Toggle(int value) {
this.value = value;
}
// @@protoc_insertion_point(enum_scope:tensorflow.RewriterConfig.Toggle)
}
/**
*
* Enum controlling the number of times to run optimizers. The default is to
* run them once.
*
*
* Protobuf enum {@code tensorflow.RewriterConfig.NumIterationsType}
*/
public enum NumIterationsType
implements org.nd4j.shade.protobuf.ProtocolMessageEnum {
/**
* DEFAULT_NUM_ITERS = 0;
*/
DEFAULT_NUM_ITERS(0),
/**
* ONE = 1;
*/
ONE(1),
/**
* TWO = 2;
*/
TWO(2),
UNRECOGNIZED(-1),
;
/**
* DEFAULT_NUM_ITERS = 0;
*/
public static final int DEFAULT_NUM_ITERS_VALUE = 0;
/**
* ONE = 1;
*/
public static final int ONE_VALUE = 1;
/**
* TWO = 2;
*/
public static final int TWO_VALUE = 2;
public final int getNumber() {
if (this == UNRECOGNIZED) {
throw new java.lang.IllegalArgumentException(
"Can't get the number of an unknown enum value.");
}
return value;
}
/**
* @deprecated Use {@link #forNumber(int)} instead.
*/
@java.lang.Deprecated
public static NumIterationsType valueOf(int value) {
return forNumber(value);
}
public static NumIterationsType forNumber(int value) {
switch (value) {
case 0: return DEFAULT_NUM_ITERS;
case 1: return ONE;
case 2: return TWO;
default: return null;
}
}
public static org.nd4j.shade.protobuf.Internal.EnumLiteMap
internalGetValueMap() {
return internalValueMap;
}
private static final org.nd4j.shade.protobuf.Internal.EnumLiteMap<
NumIterationsType> internalValueMap =
new org.nd4j.shade.protobuf.Internal.EnumLiteMap() {
public NumIterationsType findValueByNumber(int number) {
return NumIterationsType.forNumber(number);
}
};
public final org.nd4j.shade.protobuf.Descriptors.EnumValueDescriptor
getValueDescriptor() {
return getDescriptor().getValues().get(ordinal());
}
public final org.nd4j.shade.protobuf.Descriptors.EnumDescriptor
getDescriptorForType() {
return getDescriptor();
}
public static final org.nd4j.shade.protobuf.Descriptors.EnumDescriptor
getDescriptor() {
return org.tensorflow.framework.RewriterConfig.getDescriptor().getEnumTypes().get(1);
}
private static final NumIterationsType[] VALUES = values();
public static NumIterationsType valueOf(
org.nd4j.shade.protobuf.Descriptors.EnumValueDescriptor desc) {
if (desc.getType() != getDescriptor()) {
throw new java.lang.IllegalArgumentException(
"EnumValueDescriptor is not for this type.");
}
if (desc.getIndex() == -1) {
return UNRECOGNIZED;
}
return VALUES[desc.getIndex()];
}
private final int value;
private NumIterationsType(int value) {
this.value = value;
}
// @@protoc_insertion_point(enum_scope:tensorflow.RewriterConfig.NumIterationsType)
}
/**
* Protobuf enum {@code tensorflow.RewriterConfig.MemOptType}
*/
public enum MemOptType
implements org.nd4j.shade.protobuf.ProtocolMessageEnum {
/**
*
* The default setting (SCHEDULING and SWAPPING HEURISTICS only)
*
*
* DEFAULT_MEM_OPT = 0;
*/
DEFAULT_MEM_OPT(0),
/**
*
* Disabled in the meta-optimizer.
*
*
* NO_MEM_OPT = 1;
*/
NO_MEM_OPT(1),
/**
*
* Driven by manual op-level annotations.
*
*
* MANUAL = 2;
*/
MANUAL(2),
/**
*
* Swapping heuristic will move a tensor from the GPU to the CPU and move
* it back when needed to reduce peak memory usage.
*
*
* SWAPPING_HEURISTICS = 4;
*/
SWAPPING_HEURISTICS(4),
/**
*
* Recomputation heuristics will recompute ops (such as Relu activation)
* during backprop instead of storing them, reducing peak memory usage.
*
*
* RECOMPUTATION_HEURISTICS = 5;
*/
RECOMPUTATION_HEURISTICS(5),
/**
*
* Scheduling will split big ops such as AddN and try to enforce a schedule
* of the new computations that decreases peak memory usage.
*
*
* SCHEDULING_HEURISTICS = 6;
*/
SCHEDULING_HEURISTICS(6),
/**
*
* Use any combination of swapping and recomputation heuristics.
*
*
* HEURISTICS = 3;
*/
HEURISTICS(3),
UNRECOGNIZED(-1),
;
/**
*
* The default setting (SCHEDULING and SWAPPING HEURISTICS only)
*
*
* DEFAULT_MEM_OPT = 0;
*/
public static final int DEFAULT_MEM_OPT_VALUE = 0;
/**
*
* Disabled in the meta-optimizer.
*
*
* NO_MEM_OPT = 1;
*/
public static final int NO_MEM_OPT_VALUE = 1;
/**
*
* Driven by manual op-level annotations.
*
*
* MANUAL = 2;
*/
public static final int MANUAL_VALUE = 2;
/**
*
* Swapping heuristic will move a tensor from the GPU to the CPU and move
* it back when needed to reduce peak memory usage.
*
*
* SWAPPING_HEURISTICS = 4;
*/
public static final int SWAPPING_HEURISTICS_VALUE = 4;
/**
*
* Recomputation heuristics will recompute ops (such as Relu activation)
* during backprop instead of storing them, reducing peak memory usage.
*
*
* RECOMPUTATION_HEURISTICS = 5;
*/
public static final int RECOMPUTATION_HEURISTICS_VALUE = 5;
/**
*
* Scheduling will split big ops such as AddN and try to enforce a schedule
* of the new computations that decreases peak memory usage.
*
*
* SCHEDULING_HEURISTICS = 6;
*/
public static final int SCHEDULING_HEURISTICS_VALUE = 6;
/**
*
* Use any combination of swapping and recomputation heuristics.
*
*
* HEURISTICS = 3;
*/
public static final int HEURISTICS_VALUE = 3;
public final int getNumber() {
if (this == UNRECOGNIZED) {
throw new java.lang.IllegalArgumentException(
"Can't get the number of an unknown enum value.");
}
return value;
}
/**
* @deprecated Use {@link #forNumber(int)} instead.
*/
@java.lang.Deprecated
public static MemOptType valueOf(int value) {
return forNumber(value);
}
public static MemOptType forNumber(int value) {
switch (value) {
case 0: return DEFAULT_MEM_OPT;
case 1: return NO_MEM_OPT;
case 2: return MANUAL;
case 4: return SWAPPING_HEURISTICS;
case 5: return RECOMPUTATION_HEURISTICS;
case 6: return SCHEDULING_HEURISTICS;
case 3: return HEURISTICS;
default: return null;
}
}
public static org.nd4j.shade.protobuf.Internal.EnumLiteMap
internalGetValueMap() {
return internalValueMap;
}
private static final org.nd4j.shade.protobuf.Internal.EnumLiteMap<
MemOptType> internalValueMap =
new org.nd4j.shade.protobuf.Internal.EnumLiteMap() {
public MemOptType findValueByNumber(int number) {
return MemOptType.forNumber(number);
}
};
public final org.nd4j.shade.protobuf.Descriptors.EnumValueDescriptor
getValueDescriptor() {
return getDescriptor().getValues().get(ordinal());
}
public final org.nd4j.shade.protobuf.Descriptors.EnumDescriptor
getDescriptorForType() {
return getDescriptor();
}
public static final org.nd4j.shade.protobuf.Descriptors.EnumDescriptor
getDescriptor() {
return org.tensorflow.framework.RewriterConfig.getDescriptor().getEnumTypes().get(2);
}
private static final MemOptType[] VALUES = values();
public static MemOptType valueOf(
org.nd4j.shade.protobuf.Descriptors.EnumValueDescriptor desc) {
if (desc.getType() != getDescriptor()) {
throw new java.lang.IllegalArgumentException(
"EnumValueDescriptor is not for this type.");
}
if (desc.getIndex() == -1) {
return UNRECOGNIZED;
}
return VALUES[desc.getIndex()];
}
private final int value;
private MemOptType(int value) {
this.value = value;
}
// @@protoc_insertion_point(enum_scope:tensorflow.RewriterConfig.MemOptType)
}
public interface CustomGraphOptimizerOrBuilder extends
// @@protoc_insertion_point(interface_extends:tensorflow.RewriterConfig.CustomGraphOptimizer)
org.nd4j.shade.protobuf.MessageOrBuilder {
/**
* string name = 1;
*/
java.lang.String getName();
/**
* string name = 1;
*/
org.nd4j.shade.protobuf.ByteString
getNameBytes();
/**
* map<string, .tensorflow.AttrValue> parameter_map = 2;
*/
int getParameterMapCount();
/**
* map<string, .tensorflow.AttrValue> parameter_map = 2;
*/
boolean containsParameterMap(
java.lang.String key);
/**
* Use {@link #getParameterMapMap()} instead.
*/
@java.lang.Deprecated
java.util.Map
getParameterMap();
/**
* map<string, .tensorflow.AttrValue> parameter_map = 2;
*/
java.util.Map
getParameterMapMap();
/**
* map<string, .tensorflow.AttrValue> parameter_map = 2;
*/
org.tensorflow.framework.AttrValue getParameterMapOrDefault(
java.lang.String key,
org.tensorflow.framework.AttrValue defaultValue);
/**
* map<string, .tensorflow.AttrValue> parameter_map = 2;
*/
org.tensorflow.framework.AttrValue getParameterMapOrThrow(
java.lang.String key);
}
/**
*
* Message to describe custom graph optimizer and its parameters
*
*
* Protobuf type {@code tensorflow.RewriterConfig.CustomGraphOptimizer}
*/
public static final class CustomGraphOptimizer extends
org.nd4j.shade.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.RewriterConfig.CustomGraphOptimizer)
CustomGraphOptimizerOrBuilder {
private static final long serialVersionUID = 0L;
// Use CustomGraphOptimizer.newBuilder() to construct.
private CustomGraphOptimizer(org.nd4j.shade.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private CustomGraphOptimizer() {
name_ = "";
}
@java.lang.Override
@SuppressWarnings({"unused"})
protected java.lang.Object newInstance(
UnusedPrivateParameter unused) {
return new CustomGraphOptimizer();
}
@java.lang.Override
public final org.nd4j.shade.protobuf.UnknownFieldSet
getUnknownFields() {
return this.unknownFields;
}
private CustomGraphOptimizer(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
this();
if (extensionRegistry == null) {
throw new java.lang.NullPointerException();
}
int mutable_bitField0_ = 0;
org.nd4j.shade.protobuf.UnknownFieldSet.Builder unknownFields =
org.nd4j.shade.protobuf.UnknownFieldSet.newBuilder();
try {
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int tag = input.readTag();
switch (tag) {
case 0:
done = true;
break;
case 10: {
java.lang.String s = input.readStringRequireUtf8();
name_ = s;
break;
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if (!((mutable_bitField0_ & 0x00000001) != 0)) {
parameterMap_ = org.nd4j.shade.protobuf.MapField.newMapField(
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mutable_bitField0_ |= 0x00000001;
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org.nd4j.shade.protobuf.MapEntry
parameterMap__ = input.readMessage(
ParameterMapDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry);
parameterMap_.getMutableMap().put(
parameterMap__.getKey(), parameterMap__.getValue());
break;
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if (!parseUnknownField(
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done = true;
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break;
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}
}
} catch (org.nd4j.shade.protobuf.InvalidProtocolBufferException e) {
throw e.setUnfinishedMessage(this);
} catch (java.io.IOException e) {
throw new org.nd4j.shade.protobuf.InvalidProtocolBufferException(
e).setUnfinishedMessage(this);
} finally {
this.unknownFields = unknownFields.build();
makeExtensionsImmutable();
}
}
public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_CustomGraphOptimizer_descriptor;
}
@SuppressWarnings({"rawtypes"})
@java.lang.Override
protected org.nd4j.shade.protobuf.MapField internalGetMapField(
int number) {
switch (number) {
case 2:
return internalGetParameterMap();
default:
throw new RuntimeException(
"Invalid map field number: " + number);
}
}
@java.lang.Override
protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_CustomGraphOptimizer_fieldAccessorTable
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org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.class, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder.class);
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private volatile java.lang.Object name_;
/**
* string name = 1;
*/
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java.lang.Object ref = name_;
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java.lang.String s = bs.toStringUtf8();
name_ = s;
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/**
* string name = 1;
*/
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getNameBytes() {
java.lang.Object ref = name_;
if (ref instanceof java.lang.String) {
org.nd4j.shade.protobuf.ByteString b =
org.nd4j.shade.protobuf.ByteString.copyFromUtf8(
(java.lang.String) ref);
name_ = b;
return b;
} else {
return (org.nd4j.shade.protobuf.ByteString) ref;
}
}
public static final int PARAMETER_MAP_FIELD_NUMBER = 2;
private static final class ParameterMapDefaultEntryHolder {
static final org.nd4j.shade.protobuf.MapEntry<
java.lang.String, org.tensorflow.framework.AttrValue> defaultEntry =
org.nd4j.shade.protobuf.MapEntry
.newDefaultInstance(
org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_CustomGraphOptimizer_ParameterMapEntry_descriptor,
org.nd4j.shade.protobuf.WireFormat.FieldType.STRING,
"",
org.nd4j.shade.protobuf.WireFormat.FieldType.MESSAGE,
org.tensorflow.framework.AttrValue.getDefaultInstance());
}
private org.nd4j.shade.protobuf.MapField<
java.lang.String, org.tensorflow.framework.AttrValue> parameterMap_;
private org.nd4j.shade.protobuf.MapField
internalGetParameterMap() {
if (parameterMap_ == null) {
return org.nd4j.shade.protobuf.MapField.emptyMapField(
ParameterMapDefaultEntryHolder.defaultEntry);
}
return parameterMap_;
}
public int getParameterMapCount() {
return internalGetParameterMap().getMap().size();
}
/**
* map<string, .tensorflow.AttrValue> parameter_map = 2;
*/
public boolean containsParameterMap(
java.lang.String key) {
if (key == null) { throw new java.lang.NullPointerException(); }
return internalGetParameterMap().getMap().containsKey(key);
}
/**
* Use {@link #getParameterMapMap()} instead.
*/
@java.lang.Deprecated
public java.util.Map getParameterMap() {
return getParameterMapMap();
}
/**
* map<string, .tensorflow.AttrValue> parameter_map = 2;
*/
public java.util.Map getParameterMapMap() {
return internalGetParameterMap().getMap();
}
/**
* map<string, .tensorflow.AttrValue> parameter_map = 2;
*/
public org.tensorflow.framework.AttrValue getParameterMapOrDefault(
java.lang.String key,
org.tensorflow.framework.AttrValue defaultValue) {
if (key == null) { throw new java.lang.NullPointerException(); }
java.util.Map map =
internalGetParameterMap().getMap();
return map.containsKey(key) ? map.get(key) : defaultValue;
}
/**
* map<string, .tensorflow.AttrValue> parameter_map = 2;
*/
public org.tensorflow.framework.AttrValue getParameterMapOrThrow(
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org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
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org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
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public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer parseFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
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@java.lang.Override
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public static Builder newBuilder() {
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public static Builder newBuilder(org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer prototype) {
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@java.lang.Override
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/**
*
* Message to describe custom graph optimizer and its parameters
*
*
* Protobuf type {@code tensorflow.RewriterConfig.CustomGraphOptimizer}
*/
public static final class Builder extends
org.nd4j.shade.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:tensorflow.RewriterConfig.CustomGraphOptimizer)
org.tensorflow.framework.RewriterConfig.CustomGraphOptimizerOrBuilder {
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@SuppressWarnings({"rawtypes"})
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switch (number) {
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/**
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/**
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/**
* map<string, .tensorflow.AttrValue> parameter_map = 2;
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/**
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java.lang.String key,
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java.util.Map map =
internalGetParameterMap().getMap();
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}
/**
* map<string, .tensorflow.AttrValue> parameter_map = 2;
*/
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java.lang.String key) {
if (key == null) { throw new java.lang.NullPointerException(); }
java.util.Map map =
internalGetParameterMap().getMap();
if (!map.containsKey(key)) {
throw new java.lang.IllegalArgumentException();
}
return map.get(key);
}
public Builder clearParameterMap() {
internalGetMutableParameterMap().getMutableMap()
.clear();
return this;
}
/**
* map<string, .tensorflow.AttrValue> parameter_map = 2;
*/
public Builder removeParameterMap(
java.lang.String key) {
if (key == null) { throw new java.lang.NullPointerException(); }
internalGetMutableParameterMap().getMutableMap()
.remove(key);
return this;
}
/**
* Use alternate mutation accessors instead.
*/
@java.lang.Deprecated
public java.util.Map
getMutableParameterMap() {
return internalGetMutableParameterMap().getMutableMap();
}
/**
* map<string, .tensorflow.AttrValue> parameter_map = 2;
*/
public Builder putParameterMap(
java.lang.String key,
org.tensorflow.framework.AttrValue value) {
if (key == null) { throw new java.lang.NullPointerException(); }
if (value == null) { throw new java.lang.NullPointerException(); }
internalGetMutableParameterMap().getMutableMap()
.put(key, value);
return this;
}
/**
* map<string, .tensorflow.AttrValue> parameter_map = 2;
*/
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java.util.Map values) {
internalGetMutableParameterMap().getMutableMap()
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return this;
}
@java.lang.Override
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final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) {
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}
@java.lang.Override
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}
// @@protoc_insertion_point(builder_scope:tensorflow.RewriterConfig.CustomGraphOptimizer)
}
// @@protoc_insertion_point(class_scope:tensorflow.RewriterConfig.CustomGraphOptimizer)
private static final org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer();
}
public static org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final org.nd4j.shade.protobuf.Parser
PARSER = new org.nd4j.shade.protobuf.AbstractParser() {
@java.lang.Override
public CustomGraphOptimizer parsePartialFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return new CustomGraphOptimizer(input, extensionRegistry);
}
};
public static org.nd4j.shade.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public org.nd4j.shade.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}
public static final int LAYOUT_OPTIMIZER_FIELD_NUMBER = 1;
private int layoutOptimizer_;
/**
*
* Optimize tensor layouts (default is ON)
* e.g. This will try to use NCHW layout on GPU which is faster.
*
*
* .tensorflow.RewriterConfig.Toggle layout_optimizer = 1;
*/
public int getLayoutOptimizerValue() {
return layoutOptimizer_;
}
/**
*
* Optimize tensor layouts (default is ON)
* e.g. This will try to use NCHW layout on GPU which is faster.
*
*
* .tensorflow.RewriterConfig.Toggle layout_optimizer = 1;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getLayoutOptimizer() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(layoutOptimizer_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
public static final int CONSTANT_FOLDING_FIELD_NUMBER = 3;
private int constantFolding_;
/**
*
* Fold constants (default is ON)
* Statically infer the value of tensors when possible, and materialize the
* result using constants.
*
*
* .tensorflow.RewriterConfig.Toggle constant_folding = 3;
*/
public int getConstantFoldingValue() {
return constantFolding_;
}
/**
*
* Fold constants (default is ON)
* Statically infer the value of tensors when possible, and materialize the
* result using constants.
*
*
* .tensorflow.RewriterConfig.Toggle constant_folding = 3;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getConstantFolding() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(constantFolding_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
public static final int SHAPE_OPTIMIZATION_FIELD_NUMBER = 13;
private int shapeOptimization_;
/**
*
* Shape optimizations (default is ON)
* Simplify computations made on shapes.
*
*
* .tensorflow.RewriterConfig.Toggle shape_optimization = 13;
*/
public int getShapeOptimizationValue() {
return shapeOptimization_;
}
/**
*
* Shape optimizations (default is ON)
* Simplify computations made on shapes.
*
*
* .tensorflow.RewriterConfig.Toggle shape_optimization = 13;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getShapeOptimization() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(shapeOptimization_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
public static final int REMAPPING_FIELD_NUMBER = 14;
private int remapping_;
/**
*
* Remapping (default is ON)
* Remap subgraphs onto more efficient implementations.
*
*
* .tensorflow.RewriterConfig.Toggle remapping = 14;
*/
public int getRemappingValue() {
return remapping_;
}
/**
*
* Remapping (default is ON)
* Remap subgraphs onto more efficient implementations.
*
*
* .tensorflow.RewriterConfig.Toggle remapping = 14;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getRemapping() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(remapping_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
public static final int ARITHMETIC_OPTIMIZATION_FIELD_NUMBER = 7;
private int arithmeticOptimization_;
/**
*
* Arithmetic optimizations (default is ON)
* e.g. Simplify arithmetic ops; merge ops with same value (like constants).
*
*
* .tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7;
*/
public int getArithmeticOptimizationValue() {
return arithmeticOptimization_;
}
/**
*
* Arithmetic optimizations (default is ON)
* e.g. Simplify arithmetic ops; merge ops with same value (like constants).
*
*
* .tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getArithmeticOptimization() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(arithmeticOptimization_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
public static final int DEPENDENCY_OPTIMIZATION_FIELD_NUMBER = 8;
private int dependencyOptimization_;
/**
*
* Control dependency optimizations (default is ON).
* Remove redundant control dependencies, which may enable other optimization.
*
*
* .tensorflow.RewriterConfig.Toggle dependency_optimization = 8;
*/
public int getDependencyOptimizationValue() {
return dependencyOptimization_;
}
/**
*
* Control dependency optimizations (default is ON).
* Remove redundant control dependencies, which may enable other optimization.
*
*
* .tensorflow.RewriterConfig.Toggle dependency_optimization = 8;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getDependencyOptimization() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(dependencyOptimization_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
public static final int LOOP_OPTIMIZATION_FIELD_NUMBER = 9;
private int loopOptimization_;
/**
*
* Loop optimizations (default is ON).
*
*
* .tensorflow.RewriterConfig.Toggle loop_optimization = 9;
*/
public int getLoopOptimizationValue() {
return loopOptimization_;
}
/**
*
* Loop optimizations (default is ON).
*
*
* .tensorflow.RewriterConfig.Toggle loop_optimization = 9;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getLoopOptimization() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(loopOptimization_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
public static final int FUNCTION_OPTIMIZATION_FIELD_NUMBER = 10;
private int functionOptimization_;
/**
*
* Function optimizations (default is ON).
*
*
* .tensorflow.RewriterConfig.Toggle function_optimization = 10;
*/
public int getFunctionOptimizationValue() {
return functionOptimization_;
}
/**
*
* Function optimizations (default is ON).
*
*
* .tensorflow.RewriterConfig.Toggle function_optimization = 10;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getFunctionOptimization() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(functionOptimization_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
public static final int DEBUG_STRIPPER_FIELD_NUMBER = 11;
private int debugStripper_;
/**
*
* Strips debug-related nodes from the graph (off by default).
*
*
* .tensorflow.RewriterConfig.Toggle debug_stripper = 11;
*/
public int getDebugStripperValue() {
return debugStripper_;
}
/**
*
* Strips debug-related nodes from the graph (off by default).
*
*
* .tensorflow.RewriterConfig.Toggle debug_stripper = 11;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getDebugStripper() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(debugStripper_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
public static final int DISABLE_MODEL_PRUNING_FIELD_NUMBER = 2;
private boolean disableModelPruning_;
/**
*
* If true, don't remove unnecessary ops from the graph
*
*
* bool disable_model_pruning = 2;
*/
public boolean getDisableModelPruning() {
return disableModelPruning_;
}
public static final int SCOPED_ALLOCATOR_OPTIMIZATION_FIELD_NUMBER = 15;
private int scopedAllocatorOptimization_;
/**
*
* Try to allocate some independent Op outputs contiguously in order to
* merge or eliminate downstream Ops (off by default).
*
*
* .tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15;
*/
public int getScopedAllocatorOptimizationValue() {
return scopedAllocatorOptimization_;
}
/**
*
* Try to allocate some independent Op outputs contiguously in order to
* merge or eliminate downstream Ops (off by default).
*
*
* .tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getScopedAllocatorOptimization() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(scopedAllocatorOptimization_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
public static final int META_OPTIMIZER_ITERATIONS_FIELD_NUMBER = 12;
private int metaOptimizerIterations_;
/**
*
* Controls how many times we run the optimizers in meta optimizer (default
* is once).
*
*
* .tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12;
*/
public int getMetaOptimizerIterationsValue() {
return metaOptimizerIterations_;
}
/**
*
* Controls how many times we run the optimizers in meta optimizer (default
* is once).
*
*
* .tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12;
*/
public org.tensorflow.framework.RewriterConfig.NumIterationsType getMetaOptimizerIterations() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.NumIterationsType result = org.tensorflow.framework.RewriterConfig.NumIterationsType.valueOf(metaOptimizerIterations_);
return result == null ? org.tensorflow.framework.RewriterConfig.NumIterationsType.UNRECOGNIZED : result;
}
public static final int MEMORY_OPTIMIZATION_FIELD_NUMBER = 4;
private int memoryOptimization_;
/**
*
* Configures memory optimization passes through the meta-optimizer. Has no
* effect on manually requested memory optimization passes in the optimizers
* field.
*
*
* .tensorflow.RewriterConfig.MemOptType memory_optimization = 4;
*/
public int getMemoryOptimizationValue() {
return memoryOptimization_;
}
/**
*
* Configures memory optimization passes through the meta-optimizer. Has no
* effect on manually requested memory optimization passes in the optimizers
* field.
*
*
* .tensorflow.RewriterConfig.MemOptType memory_optimization = 4;
*/
public org.tensorflow.framework.RewriterConfig.MemOptType getMemoryOptimization() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.MemOptType result = org.tensorflow.framework.RewriterConfig.MemOptType.valueOf(memoryOptimization_);
return result == null ? org.tensorflow.framework.RewriterConfig.MemOptType.UNRECOGNIZED : result;
}
public static final int MEMORY_OPTIMIZER_TARGET_NODE_NAME_SCOPE_FIELD_NUMBER = 6;
private volatile java.lang.Object memoryOptimizerTargetNodeNameScope_;
/**
*
* A node name scope for node names which are valid outputs of recompuations.
* Inputs to nodes that match this scope may be recomputed (subject either to
* manual annotation of those input nodes or to manual annotation and
* heuristics depending on memory_optimization), but the nodes themselves will
* not be recomputed. This matches any sub-scopes as well, meaning the scope
* can appear not just as a top-level scope. For example, if the value is
* "gradients/", the default, it will match node name "gradients/foo",
* "foo/gradients/bar", but not "foo_gradients/"
*
*
* string memory_optimizer_target_node_name_scope = 6;
*/
public java.lang.String getMemoryOptimizerTargetNodeNameScope() {
java.lang.Object ref = memoryOptimizerTargetNodeNameScope_;
if (ref instanceof java.lang.String) {
return (java.lang.String) ref;
} else {
org.nd4j.shade.protobuf.ByteString bs =
(org.nd4j.shade.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
memoryOptimizerTargetNodeNameScope_ = s;
return s;
}
}
/**
*
* A node name scope for node names which are valid outputs of recompuations.
* Inputs to nodes that match this scope may be recomputed (subject either to
* manual annotation of those input nodes or to manual annotation and
* heuristics depending on memory_optimization), but the nodes themselves will
* not be recomputed. This matches any sub-scopes as well, meaning the scope
* can appear not just as a top-level scope. For example, if the value is
* "gradients/", the default, it will match node name "gradients/foo",
* "foo/gradients/bar", but not "foo_gradients/"
*
*
* string memory_optimizer_target_node_name_scope = 6;
*/
public org.nd4j.shade.protobuf.ByteString
getMemoryOptimizerTargetNodeNameScopeBytes() {
java.lang.Object ref = memoryOptimizerTargetNodeNameScope_;
if (ref instanceof java.lang.String) {
org.nd4j.shade.protobuf.ByteString b =
org.nd4j.shade.protobuf.ByteString.copyFromUtf8(
(java.lang.String) ref);
memoryOptimizerTargetNodeNameScope_ = b;
return b;
} else {
return (org.nd4j.shade.protobuf.ByteString) ref;
}
}
public static final int AUTO_PARALLEL_FIELD_NUMBER = 5;
private org.tensorflow.framework.AutoParallelOptions autoParallel_;
/**
*
* Configures AutoParallel optimization passes either through the
* meta-optimizer or when manually specified through the optimizers field.
*
*
* .tensorflow.AutoParallelOptions auto_parallel = 5;
*/
public boolean hasAutoParallel() {
return autoParallel_ != null;
}
/**
*
* Configures AutoParallel optimization passes either through the
* meta-optimizer or when manually specified through the optimizers field.
*
*
* .tensorflow.AutoParallelOptions auto_parallel = 5;
*/
public org.tensorflow.framework.AutoParallelOptions getAutoParallel() {
return autoParallel_ == null ? org.tensorflow.framework.AutoParallelOptions.getDefaultInstance() : autoParallel_;
}
/**
*
* Configures AutoParallel optimization passes either through the
* meta-optimizer or when manually specified through the optimizers field.
*
*
* .tensorflow.AutoParallelOptions auto_parallel = 5;
*/
public org.tensorflow.framework.AutoParallelOptionsOrBuilder getAutoParallelOrBuilder() {
return getAutoParallel();
}
public static final int SCOPED_ALLOCATOR_OPTS_FIELD_NUMBER = 16;
private org.tensorflow.framework.ScopedAllocatorOptions scopedAllocatorOpts_;
/**
* .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
*/
public boolean hasScopedAllocatorOpts() {
return scopedAllocatorOpts_ != null;
}
/**
* .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
*/
public org.tensorflow.framework.ScopedAllocatorOptions getScopedAllocatorOpts() {
return scopedAllocatorOpts_ == null ? org.tensorflow.framework.ScopedAllocatorOptions.getDefaultInstance() : scopedAllocatorOpts_;
}
/**
* .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
*/
public org.tensorflow.framework.ScopedAllocatorOptionsOrBuilder getScopedAllocatorOptsOrBuilder() {
return getScopedAllocatorOpts();
}
public static final int OPTIMIZERS_FIELD_NUMBER = 100;
private org.nd4j.shade.protobuf.LazyStringList optimizers_;
/**
*
* If non-empty, will use this as an alternative way to specify a list of
* optimizations to turn on and the order of the optimizations (replacing the
* meta-optimizer).
* Of the RewriterConfig options, only the AutoParallel configuration options
* (the auto_parallel field) apply to manually requested optimization passes
* ("autoparallel"). Memory optimization passes ("memory") invoked here are
* not configurable (in contrast to memory optimization passes through the
* meta-optimizer) and act only on manual op annotations.
* Custom registered optimizers will be run after the base optimizers, in
* the order that they are specified.
*
*
* repeated string optimizers = 100;
*/
public org.nd4j.shade.protobuf.ProtocolStringList
getOptimizersList() {
return optimizers_;
}
/**
*
* If non-empty, will use this as an alternative way to specify a list of
* optimizations to turn on and the order of the optimizations (replacing the
* meta-optimizer).
* Of the RewriterConfig options, only the AutoParallel configuration options
* (the auto_parallel field) apply to manually requested optimization passes
* ("autoparallel"). Memory optimization passes ("memory") invoked here are
* not configurable (in contrast to memory optimization passes through the
* meta-optimizer) and act only on manual op annotations.
* Custom registered optimizers will be run after the base optimizers, in
* the order that they are specified.
*
*
* repeated string optimizers = 100;
*/
public int getOptimizersCount() {
return optimizers_.size();
}
/**
*
* If non-empty, will use this as an alternative way to specify a list of
* optimizations to turn on and the order of the optimizations (replacing the
* meta-optimizer).
* Of the RewriterConfig options, only the AutoParallel configuration options
* (the auto_parallel field) apply to manually requested optimization passes
* ("autoparallel"). Memory optimization passes ("memory") invoked here are
* not configurable (in contrast to memory optimization passes through the
* meta-optimizer) and act only on manual op annotations.
* Custom registered optimizers will be run after the base optimizers, in
* the order that they are specified.
*
*
* repeated string optimizers = 100;
*/
public java.lang.String getOptimizers(int index) {
return optimizers_.get(index);
}
/**
*
* If non-empty, will use this as an alternative way to specify a list of
* optimizations to turn on and the order of the optimizations (replacing the
* meta-optimizer).
* Of the RewriterConfig options, only the AutoParallel configuration options
* (the auto_parallel field) apply to manually requested optimization passes
* ("autoparallel"). Memory optimization passes ("memory") invoked here are
* not configurable (in contrast to memory optimization passes through the
* meta-optimizer) and act only on manual op annotations.
* Custom registered optimizers will be run after the base optimizers, in
* the order that they are specified.
*
*
* repeated string optimizers = 100;
*/
public org.nd4j.shade.protobuf.ByteString
getOptimizersBytes(int index) {
return optimizers_.getByteString(index);
}
public static final int CUSTOM_OPTIMIZERS_FIELD_NUMBER = 200;
private java.util.List customOptimizers_;
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public java.util.List getCustomOptimizersList() {
return customOptimizers_;
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public java.util.List
getCustomOptimizersOrBuilderList() {
return customOptimizers_;
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public int getCustomOptimizersCount() {
return customOptimizers_.size();
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer getCustomOptimizers(int index) {
return customOptimizers_.get(index);
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizerOrBuilder getCustomOptimizersOrBuilder(
int index) {
return customOptimizers_.get(index);
}
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(org.nd4j.shade.protobuf.CodedOutputStream output)
throws java.io.IOException {
if (layoutOptimizer_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
output.writeEnum(1, layoutOptimizer_);
}
if (disableModelPruning_ != false) {
output.writeBool(2, disableModelPruning_);
}
if (constantFolding_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
output.writeEnum(3, constantFolding_);
}
if (memoryOptimization_ != org.tensorflow.framework.RewriterConfig.MemOptType.DEFAULT_MEM_OPT.getNumber()) {
output.writeEnum(4, memoryOptimization_);
}
if (autoParallel_ != null) {
output.writeMessage(5, getAutoParallel());
}
if (!getMemoryOptimizerTargetNodeNameScopeBytes().isEmpty()) {
org.nd4j.shade.protobuf.GeneratedMessageV3.writeString(output, 6, memoryOptimizerTargetNodeNameScope_);
}
if (arithmeticOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
output.writeEnum(7, arithmeticOptimization_);
}
if (dependencyOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
output.writeEnum(8, dependencyOptimization_);
}
if (loopOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
output.writeEnum(9, loopOptimization_);
}
if (functionOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
output.writeEnum(10, functionOptimization_);
}
if (debugStripper_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
output.writeEnum(11, debugStripper_);
}
if (metaOptimizerIterations_ != org.tensorflow.framework.RewriterConfig.NumIterationsType.DEFAULT_NUM_ITERS.getNumber()) {
output.writeEnum(12, metaOptimizerIterations_);
}
if (shapeOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
output.writeEnum(13, shapeOptimization_);
}
if (remapping_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
output.writeEnum(14, remapping_);
}
if (scopedAllocatorOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
output.writeEnum(15, scopedAllocatorOptimization_);
}
if (scopedAllocatorOpts_ != null) {
output.writeMessage(16, getScopedAllocatorOpts());
}
for (int i = 0; i < optimizers_.size(); i++) {
org.nd4j.shade.protobuf.GeneratedMessageV3.writeString(output, 100, optimizers_.getRaw(i));
}
for (int i = 0; i < customOptimizers_.size(); i++) {
output.writeMessage(200, customOptimizers_.get(i));
}
unknownFields.writeTo(output);
}
@java.lang.Override
public int getSerializedSize() {
int size = memoizedSize;
if (size != -1) return size;
size = 0;
if (layoutOptimizer_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeEnumSize(1, layoutOptimizer_);
}
if (disableModelPruning_ != false) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeBoolSize(2, disableModelPruning_);
}
if (constantFolding_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeEnumSize(3, constantFolding_);
}
if (memoryOptimization_ != org.tensorflow.framework.RewriterConfig.MemOptType.DEFAULT_MEM_OPT.getNumber()) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeEnumSize(4, memoryOptimization_);
}
if (autoParallel_ != null) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeMessageSize(5, getAutoParallel());
}
if (!getMemoryOptimizerTargetNodeNameScopeBytes().isEmpty()) {
size += org.nd4j.shade.protobuf.GeneratedMessageV3.computeStringSize(6, memoryOptimizerTargetNodeNameScope_);
}
if (arithmeticOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeEnumSize(7, arithmeticOptimization_);
}
if (dependencyOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeEnumSize(8, dependencyOptimization_);
}
if (loopOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeEnumSize(9, loopOptimization_);
}
if (functionOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeEnumSize(10, functionOptimization_);
}
if (debugStripper_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeEnumSize(11, debugStripper_);
}
if (metaOptimizerIterations_ != org.tensorflow.framework.RewriterConfig.NumIterationsType.DEFAULT_NUM_ITERS.getNumber()) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeEnumSize(12, metaOptimizerIterations_);
}
if (shapeOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeEnumSize(13, shapeOptimization_);
}
if (remapping_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeEnumSize(14, remapping_);
}
if (scopedAllocatorOptimization_ != org.tensorflow.framework.RewriterConfig.Toggle.DEFAULT.getNumber()) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeEnumSize(15, scopedAllocatorOptimization_);
}
if (scopedAllocatorOpts_ != null) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeMessageSize(16, getScopedAllocatorOpts());
}
{
int dataSize = 0;
for (int i = 0; i < optimizers_.size(); i++) {
dataSize += computeStringSizeNoTag(optimizers_.getRaw(i));
}
size += dataSize;
size += 2 * getOptimizersList().size();
}
for (int i = 0; i < customOptimizers_.size(); i++) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeMessageSize(200, customOptimizers_.get(i));
}
size += unknownFields.getSerializedSize();
memoizedSize = size;
return size;
}
@java.lang.Override
public boolean equals(final java.lang.Object obj) {
if (obj == this) {
return true;
}
if (!(obj instanceof org.tensorflow.framework.RewriterConfig)) {
return super.equals(obj);
}
org.tensorflow.framework.RewriterConfig other = (org.tensorflow.framework.RewriterConfig) obj;
if (layoutOptimizer_ != other.layoutOptimizer_) return false;
if (constantFolding_ != other.constantFolding_) return false;
if (shapeOptimization_ != other.shapeOptimization_) return false;
if (remapping_ != other.remapping_) return false;
if (arithmeticOptimization_ != other.arithmeticOptimization_) return false;
if (dependencyOptimization_ != other.dependencyOptimization_) return false;
if (loopOptimization_ != other.loopOptimization_) return false;
if (functionOptimization_ != other.functionOptimization_) return false;
if (debugStripper_ != other.debugStripper_) return false;
if (getDisableModelPruning()
!= other.getDisableModelPruning()) return false;
if (scopedAllocatorOptimization_ != other.scopedAllocatorOptimization_) return false;
if (metaOptimizerIterations_ != other.metaOptimizerIterations_) return false;
if (memoryOptimization_ != other.memoryOptimization_) return false;
if (!getMemoryOptimizerTargetNodeNameScope()
.equals(other.getMemoryOptimizerTargetNodeNameScope())) return false;
if (hasAutoParallel() != other.hasAutoParallel()) return false;
if (hasAutoParallel()) {
if (!getAutoParallel()
.equals(other.getAutoParallel())) return false;
}
if (hasScopedAllocatorOpts() != other.hasScopedAllocatorOpts()) return false;
if (hasScopedAllocatorOpts()) {
if (!getScopedAllocatorOpts()
.equals(other.getScopedAllocatorOpts())) return false;
}
if (!getOptimizersList()
.equals(other.getOptimizersList())) return false;
if (!getCustomOptimizersList()
.equals(other.getCustomOptimizersList())) return false;
if (!unknownFields.equals(other.unknownFields)) 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) + LAYOUT_OPTIMIZER_FIELD_NUMBER;
hash = (53 * hash) + layoutOptimizer_;
hash = (37 * hash) + CONSTANT_FOLDING_FIELD_NUMBER;
hash = (53 * hash) + constantFolding_;
hash = (37 * hash) + SHAPE_OPTIMIZATION_FIELD_NUMBER;
hash = (53 * hash) + shapeOptimization_;
hash = (37 * hash) + REMAPPING_FIELD_NUMBER;
hash = (53 * hash) + remapping_;
hash = (37 * hash) + ARITHMETIC_OPTIMIZATION_FIELD_NUMBER;
hash = (53 * hash) + arithmeticOptimization_;
hash = (37 * hash) + DEPENDENCY_OPTIMIZATION_FIELD_NUMBER;
hash = (53 * hash) + dependencyOptimization_;
hash = (37 * hash) + LOOP_OPTIMIZATION_FIELD_NUMBER;
hash = (53 * hash) + loopOptimization_;
hash = (37 * hash) + FUNCTION_OPTIMIZATION_FIELD_NUMBER;
hash = (53 * hash) + functionOptimization_;
hash = (37 * hash) + DEBUG_STRIPPER_FIELD_NUMBER;
hash = (53 * hash) + debugStripper_;
hash = (37 * hash) + DISABLE_MODEL_PRUNING_FIELD_NUMBER;
hash = (53 * hash) + org.nd4j.shade.protobuf.Internal.hashBoolean(
getDisableModelPruning());
hash = (37 * hash) + SCOPED_ALLOCATOR_OPTIMIZATION_FIELD_NUMBER;
hash = (53 * hash) + scopedAllocatorOptimization_;
hash = (37 * hash) + META_OPTIMIZER_ITERATIONS_FIELD_NUMBER;
hash = (53 * hash) + metaOptimizerIterations_;
hash = (37 * hash) + MEMORY_OPTIMIZATION_FIELD_NUMBER;
hash = (53 * hash) + memoryOptimization_;
hash = (37 * hash) + MEMORY_OPTIMIZER_TARGET_NODE_NAME_SCOPE_FIELD_NUMBER;
hash = (53 * hash) + getMemoryOptimizerTargetNodeNameScope().hashCode();
if (hasAutoParallel()) {
hash = (37 * hash) + AUTO_PARALLEL_FIELD_NUMBER;
hash = (53 * hash) + getAutoParallel().hashCode();
}
if (hasScopedAllocatorOpts()) {
hash = (37 * hash) + SCOPED_ALLOCATOR_OPTS_FIELD_NUMBER;
hash = (53 * hash) + getScopedAllocatorOpts().hashCode();
}
if (getOptimizersCount() > 0) {
hash = (37 * hash) + OPTIMIZERS_FIELD_NUMBER;
hash = (53 * hash) + getOptimizersList().hashCode();
}
if (getCustomOptimizersCount() > 0) {
hash = (37 * hash) + CUSTOM_OPTIMIZERS_FIELD_NUMBER;
hash = (53 * hash) + getCustomOptimizersList().hashCode();
}
hash = (29 * hash) + unknownFields.hashCode();
memoizedHashCode = hash;
return hash;
}
public static org.tensorflow.framework.RewriterConfig parseFrom(
java.nio.ByteBuffer data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.RewriterConfig parseFrom(
java.nio.ByteBuffer data,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static org.tensorflow.framework.RewriterConfig parseFrom(
org.nd4j.shade.protobuf.ByteString data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.RewriterConfig parseFrom(
org.nd4j.shade.protobuf.ByteString data,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static org.tensorflow.framework.RewriterConfig parseFrom(byte[] data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.RewriterConfig parseFrom(
byte[] data,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static org.tensorflow.framework.RewriterConfig parseFrom(java.io.InputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
.parseWithIOException(PARSER, input);
}
public static org.tensorflow.framework.RewriterConfig parseFrom(
java.io.InputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
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public static org.tensorflow.framework.RewriterConfig parseDelimitedFrom(java.io.InputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
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public static org.tensorflow.framework.RewriterConfig parseDelimitedFrom(
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org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
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return org.nd4j.shade.protobuf.GeneratedMessageV3
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public static org.tensorflow.framework.RewriterConfig parseFrom(
org.nd4j.shade.protobuf.CodedInputStream input)
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return org.nd4j.shade.protobuf.GeneratedMessageV3
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public static org.tensorflow.framework.RewriterConfig parseFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
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@java.lang.Override
public Builder newBuilderForType() { return newBuilder(); }
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
}
public static Builder newBuilder(org.tensorflow.framework.RewriterConfig prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
}
@java.lang.Override
public Builder toBuilder() {
return this == DEFAULT_INSTANCE
? new Builder() : new Builder().mergeFrom(this);
}
@java.lang.Override
protected Builder newBuilderForType(
org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
* Graph rewriting is experimental and subject to change, not covered by any
* API stability guarantees.
*
*
* Protobuf type {@code tensorflow.RewriterConfig}
*/
public static final class Builder extends
org.nd4j.shade.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:tensorflow.RewriterConfig)
org.tensorflow.framework.RewriterConfigOrBuilder {
public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.RewriterConfigProtos.internal_static_tensorflow_RewriterConfig_descriptor;
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private Builder() {
maybeForceBuilderInitialization();
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private Builder(
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super.clear();
layoutOptimizer_ = 0;
constantFolding_ = 0;
shapeOptimization_ = 0;
remapping_ = 0;
arithmeticOptimization_ = 0;
dependencyOptimization_ = 0;
loopOptimization_ = 0;
functionOptimization_ = 0;
debugStripper_ = 0;
disableModelPruning_ = false;
scopedAllocatorOptimization_ = 0;
metaOptimizerIterations_ = 0;
memoryOptimization_ = 0;
memoryOptimizerTargetNodeNameScope_ = "";
if (autoParallelBuilder_ == null) {
autoParallel_ = null;
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autoParallel_ = null;
autoParallelBuilder_ = null;
}
if (scopedAllocatorOptsBuilder_ == null) {
scopedAllocatorOpts_ = null;
} else {
scopedAllocatorOpts_ = null;
scopedAllocatorOptsBuilder_ = null;
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optimizers_ = org.nd4j.shade.protobuf.LazyStringArrayList.EMPTY;
bitField0_ = (bitField0_ & ~0x00000001);
if (customOptimizersBuilder_ == null) {
customOptimizers_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000002);
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@java.lang.Override
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@java.lang.Override
public org.tensorflow.framework.RewriterConfig getDefaultInstanceForType() {
return org.tensorflow.framework.RewriterConfig.getDefaultInstance();
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org.tensorflow.framework.RewriterConfig result = buildPartial();
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}
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@java.lang.Override
public org.tensorflow.framework.RewriterConfig buildPartial() {
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int from_bitField0_ = bitField0_;
result.layoutOptimizer_ = layoutOptimizer_;
result.constantFolding_ = constantFolding_;
result.shapeOptimization_ = shapeOptimization_;
result.remapping_ = remapping_;
result.arithmeticOptimization_ = arithmeticOptimization_;
result.dependencyOptimization_ = dependencyOptimization_;
result.loopOptimization_ = loopOptimization_;
result.functionOptimization_ = functionOptimization_;
result.debugStripper_ = debugStripper_;
result.disableModelPruning_ = disableModelPruning_;
result.scopedAllocatorOptimization_ = scopedAllocatorOptimization_;
result.metaOptimizerIterations_ = metaOptimizerIterations_;
result.memoryOptimization_ = memoryOptimization_;
result.memoryOptimizerTargetNodeNameScope_ = memoryOptimizerTargetNodeNameScope_;
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@java.lang.Override
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@java.lang.Override
public Builder setField(
org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
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org.nd4j.shade.protobuf.Descriptors.OneofDescriptor oneof) {
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@java.lang.Override
public Builder mergeFrom(org.nd4j.shade.protobuf.Message other) {
if (other instanceof org.tensorflow.framework.RewriterConfig) {
return mergeFrom((org.tensorflow.framework.RewriterConfig)other);
} else {
super.mergeFrom(other);
return this;
}
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public Builder mergeFrom(org.tensorflow.framework.RewriterConfig other) {
if (other == org.tensorflow.framework.RewriterConfig.getDefaultInstance()) return this;
if (other.layoutOptimizer_ != 0) {
setLayoutOptimizerValue(other.getLayoutOptimizerValue());
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this.mergeUnknownFields(other.unknownFields);
onChanged();
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@java.lang.Override
public final boolean isInitialized() {
return true;
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@java.lang.Override
public Builder mergeFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
org.tensorflow.framework.RewriterConfig parsedMessage = null;
try {
parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry);
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parsedMessage = (org.tensorflow.framework.RewriterConfig) e.getUnfinishedMessage();
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private int bitField0_;
private int layoutOptimizer_ = 0;
/**
*
* Optimize tensor layouts (default is ON)
* e.g. This will try to use NCHW layout on GPU which is faster.
*
*
* .tensorflow.RewriterConfig.Toggle layout_optimizer = 1;
*/
public int getLayoutOptimizerValue() {
return layoutOptimizer_;
}
/**
*
* Optimize tensor layouts (default is ON)
* e.g. This will try to use NCHW layout on GPU which is faster.
*
*
* .tensorflow.RewriterConfig.Toggle layout_optimizer = 1;
*/
public Builder setLayoutOptimizerValue(int value) {
layoutOptimizer_ = value;
onChanged();
return this;
}
/**
*
* Optimize tensor layouts (default is ON)
* e.g. This will try to use NCHW layout on GPU which is faster.
*
*
* .tensorflow.RewriterConfig.Toggle layout_optimizer = 1;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getLayoutOptimizer() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(layoutOptimizer_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
/**
*
* Optimize tensor layouts (default is ON)
* e.g. This will try to use NCHW layout on GPU which is faster.
*
*
* .tensorflow.RewriterConfig.Toggle layout_optimizer = 1;
*/
public Builder setLayoutOptimizer(org.tensorflow.framework.RewriterConfig.Toggle value) {
if (value == null) {
throw new NullPointerException();
}
layoutOptimizer_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Optimize tensor layouts (default is ON)
* e.g. This will try to use NCHW layout on GPU which is faster.
*
*
* .tensorflow.RewriterConfig.Toggle layout_optimizer = 1;
*/
public Builder clearLayoutOptimizer() {
layoutOptimizer_ = 0;
onChanged();
return this;
}
private int constantFolding_ = 0;
/**
*
* Fold constants (default is ON)
* Statically infer the value of tensors when possible, and materialize the
* result using constants.
*
*
* .tensorflow.RewriterConfig.Toggle constant_folding = 3;
*/
public int getConstantFoldingValue() {
return constantFolding_;
}
/**
*
* Fold constants (default is ON)
* Statically infer the value of tensors when possible, and materialize the
* result using constants.
*
*
* .tensorflow.RewriterConfig.Toggle constant_folding = 3;
*/
public Builder setConstantFoldingValue(int value) {
constantFolding_ = value;
onChanged();
return this;
}
/**
*
* Fold constants (default is ON)
* Statically infer the value of tensors when possible, and materialize the
* result using constants.
*
*
* .tensorflow.RewriterConfig.Toggle constant_folding = 3;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getConstantFolding() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(constantFolding_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
/**
*
* Fold constants (default is ON)
* Statically infer the value of tensors when possible, and materialize the
* result using constants.
*
*
* .tensorflow.RewriterConfig.Toggle constant_folding = 3;
*/
public Builder setConstantFolding(org.tensorflow.framework.RewriterConfig.Toggle value) {
if (value == null) {
throw new NullPointerException();
}
constantFolding_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Fold constants (default is ON)
* Statically infer the value of tensors when possible, and materialize the
* result using constants.
*
*
* .tensorflow.RewriterConfig.Toggle constant_folding = 3;
*/
public Builder clearConstantFolding() {
constantFolding_ = 0;
onChanged();
return this;
}
private int shapeOptimization_ = 0;
/**
*
* Shape optimizations (default is ON)
* Simplify computations made on shapes.
*
*
* .tensorflow.RewriterConfig.Toggle shape_optimization = 13;
*/
public int getShapeOptimizationValue() {
return shapeOptimization_;
}
/**
*
* Shape optimizations (default is ON)
* Simplify computations made on shapes.
*
*
* .tensorflow.RewriterConfig.Toggle shape_optimization = 13;
*/
public Builder setShapeOptimizationValue(int value) {
shapeOptimization_ = value;
onChanged();
return this;
}
/**
*
* Shape optimizations (default is ON)
* Simplify computations made on shapes.
*
*
* .tensorflow.RewriterConfig.Toggle shape_optimization = 13;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getShapeOptimization() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(shapeOptimization_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
/**
*
* Shape optimizations (default is ON)
* Simplify computations made on shapes.
*
*
* .tensorflow.RewriterConfig.Toggle shape_optimization = 13;
*/
public Builder setShapeOptimization(org.tensorflow.framework.RewriterConfig.Toggle value) {
if (value == null) {
throw new NullPointerException();
}
shapeOptimization_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Shape optimizations (default is ON)
* Simplify computations made on shapes.
*
*
* .tensorflow.RewriterConfig.Toggle shape_optimization = 13;
*/
public Builder clearShapeOptimization() {
shapeOptimization_ = 0;
onChanged();
return this;
}
private int remapping_ = 0;
/**
*
* Remapping (default is ON)
* Remap subgraphs onto more efficient implementations.
*
*
* .tensorflow.RewriterConfig.Toggle remapping = 14;
*/
public int getRemappingValue() {
return remapping_;
}
/**
*
* Remapping (default is ON)
* Remap subgraphs onto more efficient implementations.
*
*
* .tensorflow.RewriterConfig.Toggle remapping = 14;
*/
public Builder setRemappingValue(int value) {
remapping_ = value;
onChanged();
return this;
}
/**
*
* Remapping (default is ON)
* Remap subgraphs onto more efficient implementations.
*
*
* .tensorflow.RewriterConfig.Toggle remapping = 14;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getRemapping() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(remapping_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
/**
*
* Remapping (default is ON)
* Remap subgraphs onto more efficient implementations.
*
*
* .tensorflow.RewriterConfig.Toggle remapping = 14;
*/
public Builder setRemapping(org.tensorflow.framework.RewriterConfig.Toggle value) {
if (value == null) {
throw new NullPointerException();
}
remapping_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Remapping (default is ON)
* Remap subgraphs onto more efficient implementations.
*
*
* .tensorflow.RewriterConfig.Toggle remapping = 14;
*/
public Builder clearRemapping() {
remapping_ = 0;
onChanged();
return this;
}
private int arithmeticOptimization_ = 0;
/**
*
* Arithmetic optimizations (default is ON)
* e.g. Simplify arithmetic ops; merge ops with same value (like constants).
*
*
* .tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7;
*/
public int getArithmeticOptimizationValue() {
return arithmeticOptimization_;
}
/**
*
* Arithmetic optimizations (default is ON)
* e.g. Simplify arithmetic ops; merge ops with same value (like constants).
*
*
* .tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7;
*/
public Builder setArithmeticOptimizationValue(int value) {
arithmeticOptimization_ = value;
onChanged();
return this;
}
/**
*
* Arithmetic optimizations (default is ON)
* e.g. Simplify arithmetic ops; merge ops with same value (like constants).
*
*
* .tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getArithmeticOptimization() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(arithmeticOptimization_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
/**
*
* Arithmetic optimizations (default is ON)
* e.g. Simplify arithmetic ops; merge ops with same value (like constants).
*
*
* .tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7;
*/
public Builder setArithmeticOptimization(org.tensorflow.framework.RewriterConfig.Toggle value) {
if (value == null) {
throw new NullPointerException();
}
arithmeticOptimization_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Arithmetic optimizations (default is ON)
* e.g. Simplify arithmetic ops; merge ops with same value (like constants).
*
*
* .tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7;
*/
public Builder clearArithmeticOptimization() {
arithmeticOptimization_ = 0;
onChanged();
return this;
}
private int dependencyOptimization_ = 0;
/**
*
* Control dependency optimizations (default is ON).
* Remove redundant control dependencies, which may enable other optimization.
*
*
* .tensorflow.RewriterConfig.Toggle dependency_optimization = 8;
*/
public int getDependencyOptimizationValue() {
return dependencyOptimization_;
}
/**
*
* Control dependency optimizations (default is ON).
* Remove redundant control dependencies, which may enable other optimization.
*
*
* .tensorflow.RewriterConfig.Toggle dependency_optimization = 8;
*/
public Builder setDependencyOptimizationValue(int value) {
dependencyOptimization_ = value;
onChanged();
return this;
}
/**
*
* Control dependency optimizations (default is ON).
* Remove redundant control dependencies, which may enable other optimization.
*
*
* .tensorflow.RewriterConfig.Toggle dependency_optimization = 8;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getDependencyOptimization() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(dependencyOptimization_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
/**
*
* Control dependency optimizations (default is ON).
* Remove redundant control dependencies, which may enable other optimization.
*
*
* .tensorflow.RewriterConfig.Toggle dependency_optimization = 8;
*/
public Builder setDependencyOptimization(org.tensorflow.framework.RewriterConfig.Toggle value) {
if (value == null) {
throw new NullPointerException();
}
dependencyOptimization_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Control dependency optimizations (default is ON).
* Remove redundant control dependencies, which may enable other optimization.
*
*
* .tensorflow.RewriterConfig.Toggle dependency_optimization = 8;
*/
public Builder clearDependencyOptimization() {
dependencyOptimization_ = 0;
onChanged();
return this;
}
private int loopOptimization_ = 0;
/**
*
* Loop optimizations (default is ON).
*
*
* .tensorflow.RewriterConfig.Toggle loop_optimization = 9;
*/
public int getLoopOptimizationValue() {
return loopOptimization_;
}
/**
*
* Loop optimizations (default is ON).
*
*
* .tensorflow.RewriterConfig.Toggle loop_optimization = 9;
*/
public Builder setLoopOptimizationValue(int value) {
loopOptimization_ = value;
onChanged();
return this;
}
/**
*
* Loop optimizations (default is ON).
*
*
* .tensorflow.RewriterConfig.Toggle loop_optimization = 9;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getLoopOptimization() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(loopOptimization_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
/**
*
* Loop optimizations (default is ON).
*
*
* .tensorflow.RewriterConfig.Toggle loop_optimization = 9;
*/
public Builder setLoopOptimization(org.tensorflow.framework.RewriterConfig.Toggle value) {
if (value == null) {
throw new NullPointerException();
}
loopOptimization_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Loop optimizations (default is ON).
*
*
* .tensorflow.RewriterConfig.Toggle loop_optimization = 9;
*/
public Builder clearLoopOptimization() {
loopOptimization_ = 0;
onChanged();
return this;
}
private int functionOptimization_ = 0;
/**
*
* Function optimizations (default is ON).
*
*
* .tensorflow.RewriterConfig.Toggle function_optimization = 10;
*/
public int getFunctionOptimizationValue() {
return functionOptimization_;
}
/**
*
* Function optimizations (default is ON).
*
*
* .tensorflow.RewriterConfig.Toggle function_optimization = 10;
*/
public Builder setFunctionOptimizationValue(int value) {
functionOptimization_ = value;
onChanged();
return this;
}
/**
*
* Function optimizations (default is ON).
*
*
* .tensorflow.RewriterConfig.Toggle function_optimization = 10;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getFunctionOptimization() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(functionOptimization_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
/**
*
* Function optimizations (default is ON).
*
*
* .tensorflow.RewriterConfig.Toggle function_optimization = 10;
*/
public Builder setFunctionOptimization(org.tensorflow.framework.RewriterConfig.Toggle value) {
if (value == null) {
throw new NullPointerException();
}
functionOptimization_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Function optimizations (default is ON).
*
*
* .tensorflow.RewriterConfig.Toggle function_optimization = 10;
*/
public Builder clearFunctionOptimization() {
functionOptimization_ = 0;
onChanged();
return this;
}
private int debugStripper_ = 0;
/**
*
* Strips debug-related nodes from the graph (off by default).
*
*
* .tensorflow.RewriterConfig.Toggle debug_stripper = 11;
*/
public int getDebugStripperValue() {
return debugStripper_;
}
/**
*
* Strips debug-related nodes from the graph (off by default).
*
*
* .tensorflow.RewriterConfig.Toggle debug_stripper = 11;
*/
public Builder setDebugStripperValue(int value) {
debugStripper_ = value;
onChanged();
return this;
}
/**
*
* Strips debug-related nodes from the graph (off by default).
*
*
* .tensorflow.RewriterConfig.Toggle debug_stripper = 11;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getDebugStripper() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(debugStripper_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
/**
*
* Strips debug-related nodes from the graph (off by default).
*
*
* .tensorflow.RewriterConfig.Toggle debug_stripper = 11;
*/
public Builder setDebugStripper(org.tensorflow.framework.RewriterConfig.Toggle value) {
if (value == null) {
throw new NullPointerException();
}
debugStripper_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Strips debug-related nodes from the graph (off by default).
*
*
* .tensorflow.RewriterConfig.Toggle debug_stripper = 11;
*/
public Builder clearDebugStripper() {
debugStripper_ = 0;
onChanged();
return this;
}
private boolean disableModelPruning_ ;
/**
*
* If true, don't remove unnecessary ops from the graph
*
*
* bool disable_model_pruning = 2;
*/
public boolean getDisableModelPruning() {
return disableModelPruning_;
}
/**
*
* If true, don't remove unnecessary ops from the graph
*
*
* bool disable_model_pruning = 2;
*/
public Builder setDisableModelPruning(boolean value) {
disableModelPruning_ = value;
onChanged();
return this;
}
/**
*
* If true, don't remove unnecessary ops from the graph
*
*
* bool disable_model_pruning = 2;
*/
public Builder clearDisableModelPruning() {
disableModelPruning_ = false;
onChanged();
return this;
}
private int scopedAllocatorOptimization_ = 0;
/**
*
* Try to allocate some independent Op outputs contiguously in order to
* merge or eliminate downstream Ops (off by default).
*
*
* .tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15;
*/
public int getScopedAllocatorOptimizationValue() {
return scopedAllocatorOptimization_;
}
/**
*
* Try to allocate some independent Op outputs contiguously in order to
* merge or eliminate downstream Ops (off by default).
*
*
* .tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15;
*/
public Builder setScopedAllocatorOptimizationValue(int value) {
scopedAllocatorOptimization_ = value;
onChanged();
return this;
}
/**
*
* Try to allocate some independent Op outputs contiguously in order to
* merge or eliminate downstream Ops (off by default).
*
*
* .tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15;
*/
public org.tensorflow.framework.RewriterConfig.Toggle getScopedAllocatorOptimization() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.Toggle result = org.tensorflow.framework.RewriterConfig.Toggle.valueOf(scopedAllocatorOptimization_);
return result == null ? org.tensorflow.framework.RewriterConfig.Toggle.UNRECOGNIZED : result;
}
/**
*
* Try to allocate some independent Op outputs contiguously in order to
* merge or eliminate downstream Ops (off by default).
*
*
* .tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15;
*/
public Builder setScopedAllocatorOptimization(org.tensorflow.framework.RewriterConfig.Toggle value) {
if (value == null) {
throw new NullPointerException();
}
scopedAllocatorOptimization_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Try to allocate some independent Op outputs contiguously in order to
* merge or eliminate downstream Ops (off by default).
*
*
* .tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15;
*/
public Builder clearScopedAllocatorOptimization() {
scopedAllocatorOptimization_ = 0;
onChanged();
return this;
}
private int metaOptimizerIterations_ = 0;
/**
*
* Controls how many times we run the optimizers in meta optimizer (default
* is once).
*
*
* .tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12;
*/
public int getMetaOptimizerIterationsValue() {
return metaOptimizerIterations_;
}
/**
*
* Controls how many times we run the optimizers in meta optimizer (default
* is once).
*
*
* .tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12;
*/
public Builder setMetaOptimizerIterationsValue(int value) {
metaOptimizerIterations_ = value;
onChanged();
return this;
}
/**
*
* Controls how many times we run the optimizers in meta optimizer (default
* is once).
*
*
* .tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12;
*/
public org.tensorflow.framework.RewriterConfig.NumIterationsType getMetaOptimizerIterations() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.NumIterationsType result = org.tensorflow.framework.RewriterConfig.NumIterationsType.valueOf(metaOptimizerIterations_);
return result == null ? org.tensorflow.framework.RewriterConfig.NumIterationsType.UNRECOGNIZED : result;
}
/**
*
* Controls how many times we run the optimizers in meta optimizer (default
* is once).
*
*
* .tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12;
*/
public Builder setMetaOptimizerIterations(org.tensorflow.framework.RewriterConfig.NumIterationsType value) {
if (value == null) {
throw new NullPointerException();
}
metaOptimizerIterations_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Controls how many times we run the optimizers in meta optimizer (default
* is once).
*
*
* .tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12;
*/
public Builder clearMetaOptimizerIterations() {
metaOptimizerIterations_ = 0;
onChanged();
return this;
}
private int memoryOptimization_ = 0;
/**
*
* Configures memory optimization passes through the meta-optimizer. Has no
* effect on manually requested memory optimization passes in the optimizers
* field.
*
*
* .tensorflow.RewriterConfig.MemOptType memory_optimization = 4;
*/
public int getMemoryOptimizationValue() {
return memoryOptimization_;
}
/**
*
* Configures memory optimization passes through the meta-optimizer. Has no
* effect on manually requested memory optimization passes in the optimizers
* field.
*
*
* .tensorflow.RewriterConfig.MemOptType memory_optimization = 4;
*/
public Builder setMemoryOptimizationValue(int value) {
memoryOptimization_ = value;
onChanged();
return this;
}
/**
*
* Configures memory optimization passes through the meta-optimizer. Has no
* effect on manually requested memory optimization passes in the optimizers
* field.
*
*
* .tensorflow.RewriterConfig.MemOptType memory_optimization = 4;
*/
public org.tensorflow.framework.RewriterConfig.MemOptType getMemoryOptimization() {
@SuppressWarnings("deprecation")
org.tensorflow.framework.RewriterConfig.MemOptType result = org.tensorflow.framework.RewriterConfig.MemOptType.valueOf(memoryOptimization_);
return result == null ? org.tensorflow.framework.RewriterConfig.MemOptType.UNRECOGNIZED : result;
}
/**
*
* Configures memory optimization passes through the meta-optimizer. Has no
* effect on manually requested memory optimization passes in the optimizers
* field.
*
*
* .tensorflow.RewriterConfig.MemOptType memory_optimization = 4;
*/
public Builder setMemoryOptimization(org.tensorflow.framework.RewriterConfig.MemOptType value) {
if (value == null) {
throw new NullPointerException();
}
memoryOptimization_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Configures memory optimization passes through the meta-optimizer. Has no
* effect on manually requested memory optimization passes in the optimizers
* field.
*
*
* .tensorflow.RewriterConfig.MemOptType memory_optimization = 4;
*/
public Builder clearMemoryOptimization() {
memoryOptimization_ = 0;
onChanged();
return this;
}
private java.lang.Object memoryOptimizerTargetNodeNameScope_ = "";
/**
*
* A node name scope for node names which are valid outputs of recompuations.
* Inputs to nodes that match this scope may be recomputed (subject either to
* manual annotation of those input nodes or to manual annotation and
* heuristics depending on memory_optimization), but the nodes themselves will
* not be recomputed. This matches any sub-scopes as well, meaning the scope
* can appear not just as a top-level scope. For example, if the value is
* "gradients/", the default, it will match node name "gradients/foo",
* "foo/gradients/bar", but not "foo_gradients/"
*
*
* string memory_optimizer_target_node_name_scope = 6;
*/
public java.lang.String getMemoryOptimizerTargetNodeNameScope() {
java.lang.Object ref = memoryOptimizerTargetNodeNameScope_;
if (!(ref instanceof java.lang.String)) {
org.nd4j.shade.protobuf.ByteString bs =
(org.nd4j.shade.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
memoryOptimizerTargetNodeNameScope_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
* A node name scope for node names which are valid outputs of recompuations.
* Inputs to nodes that match this scope may be recomputed (subject either to
* manual annotation of those input nodes or to manual annotation and
* heuristics depending on memory_optimization), but the nodes themselves will
* not be recomputed. This matches any sub-scopes as well, meaning the scope
* can appear not just as a top-level scope. For example, if the value is
* "gradients/", the default, it will match node name "gradients/foo",
* "foo/gradients/bar", but not "foo_gradients/"
*
*
* string memory_optimizer_target_node_name_scope = 6;
*/
public org.nd4j.shade.protobuf.ByteString
getMemoryOptimizerTargetNodeNameScopeBytes() {
java.lang.Object ref = memoryOptimizerTargetNodeNameScope_;
if (ref instanceof String) {
org.nd4j.shade.protobuf.ByteString b =
org.nd4j.shade.protobuf.ByteString.copyFromUtf8(
(java.lang.String) ref);
memoryOptimizerTargetNodeNameScope_ = b;
return b;
} else {
return (org.nd4j.shade.protobuf.ByteString) ref;
}
}
/**
*
* A node name scope for node names which are valid outputs of recompuations.
* Inputs to nodes that match this scope may be recomputed (subject either to
* manual annotation of those input nodes or to manual annotation and
* heuristics depending on memory_optimization), but the nodes themselves will
* not be recomputed. This matches any sub-scopes as well, meaning the scope
* can appear not just as a top-level scope. For example, if the value is
* "gradients/", the default, it will match node name "gradients/foo",
* "foo/gradients/bar", but not "foo_gradients/"
*
*
* string memory_optimizer_target_node_name_scope = 6;
*/
public Builder setMemoryOptimizerTargetNodeNameScope(
java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
memoryOptimizerTargetNodeNameScope_ = value;
onChanged();
return this;
}
/**
*
* A node name scope for node names which are valid outputs of recompuations.
* Inputs to nodes that match this scope may be recomputed (subject either to
* manual annotation of those input nodes or to manual annotation and
* heuristics depending on memory_optimization), but the nodes themselves will
* not be recomputed. This matches any sub-scopes as well, meaning the scope
* can appear not just as a top-level scope. For example, if the value is
* "gradients/", the default, it will match node name "gradients/foo",
* "foo/gradients/bar", but not "foo_gradients/"
*
*
* string memory_optimizer_target_node_name_scope = 6;
*/
public Builder clearMemoryOptimizerTargetNodeNameScope() {
memoryOptimizerTargetNodeNameScope_ = getDefaultInstance().getMemoryOptimizerTargetNodeNameScope();
onChanged();
return this;
}
/**
*
* A node name scope for node names which are valid outputs of recompuations.
* Inputs to nodes that match this scope may be recomputed (subject either to
* manual annotation of those input nodes or to manual annotation and
* heuristics depending on memory_optimization), but the nodes themselves will
* not be recomputed. This matches any sub-scopes as well, meaning the scope
* can appear not just as a top-level scope. For example, if the value is
* "gradients/", the default, it will match node name "gradients/foo",
* "foo/gradients/bar", but not "foo_gradients/"
*
*
* string memory_optimizer_target_node_name_scope = 6;
*/
public Builder setMemoryOptimizerTargetNodeNameScopeBytes(
org.nd4j.shade.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
memoryOptimizerTargetNodeNameScope_ = value;
onChanged();
return this;
}
private org.tensorflow.framework.AutoParallelOptions autoParallel_;
private org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.AutoParallelOptions, org.tensorflow.framework.AutoParallelOptions.Builder, org.tensorflow.framework.AutoParallelOptionsOrBuilder> autoParallelBuilder_;
/**
*
* Configures AutoParallel optimization passes either through the
* meta-optimizer or when manually specified through the optimizers field.
*
*
* .tensorflow.AutoParallelOptions auto_parallel = 5;
*/
public boolean hasAutoParallel() {
return autoParallelBuilder_ != null || autoParallel_ != null;
}
/**
*
* Configures AutoParallel optimization passes either through the
* meta-optimizer or when manually specified through the optimizers field.
*
*
* .tensorflow.AutoParallelOptions auto_parallel = 5;
*/
public org.tensorflow.framework.AutoParallelOptions getAutoParallel() {
if (autoParallelBuilder_ == null) {
return autoParallel_ == null ? org.tensorflow.framework.AutoParallelOptions.getDefaultInstance() : autoParallel_;
} else {
return autoParallelBuilder_.getMessage();
}
}
/**
*
* Configures AutoParallel optimization passes either through the
* meta-optimizer or when manually specified through the optimizers field.
*
*
* .tensorflow.AutoParallelOptions auto_parallel = 5;
*/
public Builder setAutoParallel(org.tensorflow.framework.AutoParallelOptions value) {
if (autoParallelBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
autoParallel_ = value;
onChanged();
} else {
autoParallelBuilder_.setMessage(value);
}
return this;
}
/**
*
* Configures AutoParallel optimization passes either through the
* meta-optimizer or when manually specified through the optimizers field.
*
*
* .tensorflow.AutoParallelOptions auto_parallel = 5;
*/
public Builder setAutoParallel(
org.tensorflow.framework.AutoParallelOptions.Builder builderForValue) {
if (autoParallelBuilder_ == null) {
autoParallel_ = builderForValue.build();
onChanged();
} else {
autoParallelBuilder_.setMessage(builderForValue.build());
}
return this;
}
/**
*
* Configures AutoParallel optimization passes either through the
* meta-optimizer or when manually specified through the optimizers field.
*
*
* .tensorflow.AutoParallelOptions auto_parallel = 5;
*/
public Builder mergeAutoParallel(org.tensorflow.framework.AutoParallelOptions value) {
if (autoParallelBuilder_ == null) {
if (autoParallel_ != null) {
autoParallel_ =
org.tensorflow.framework.AutoParallelOptions.newBuilder(autoParallel_).mergeFrom(value).buildPartial();
} else {
autoParallel_ = value;
}
onChanged();
} else {
autoParallelBuilder_.mergeFrom(value);
}
return this;
}
/**
*
* Configures AutoParallel optimization passes either through the
* meta-optimizer or when manually specified through the optimizers field.
*
*
* .tensorflow.AutoParallelOptions auto_parallel = 5;
*/
public Builder clearAutoParallel() {
if (autoParallelBuilder_ == null) {
autoParallel_ = null;
onChanged();
} else {
autoParallel_ = null;
autoParallelBuilder_ = null;
}
return this;
}
/**
*
* Configures AutoParallel optimization passes either through the
* meta-optimizer or when manually specified through the optimizers field.
*
*
* .tensorflow.AutoParallelOptions auto_parallel = 5;
*/
public org.tensorflow.framework.AutoParallelOptions.Builder getAutoParallelBuilder() {
onChanged();
return getAutoParallelFieldBuilder().getBuilder();
}
/**
*
* Configures AutoParallel optimization passes either through the
* meta-optimizer or when manually specified through the optimizers field.
*
*
* .tensorflow.AutoParallelOptions auto_parallel = 5;
*/
public org.tensorflow.framework.AutoParallelOptionsOrBuilder getAutoParallelOrBuilder() {
if (autoParallelBuilder_ != null) {
return autoParallelBuilder_.getMessageOrBuilder();
} else {
return autoParallel_ == null ?
org.tensorflow.framework.AutoParallelOptions.getDefaultInstance() : autoParallel_;
}
}
/**
*
* Configures AutoParallel optimization passes either through the
* meta-optimizer or when manually specified through the optimizers field.
*
*
* .tensorflow.AutoParallelOptions auto_parallel = 5;
*/
private org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.AutoParallelOptions, org.tensorflow.framework.AutoParallelOptions.Builder, org.tensorflow.framework.AutoParallelOptionsOrBuilder>
getAutoParallelFieldBuilder() {
if (autoParallelBuilder_ == null) {
autoParallelBuilder_ = new org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.AutoParallelOptions, org.tensorflow.framework.AutoParallelOptions.Builder, org.tensorflow.framework.AutoParallelOptionsOrBuilder>(
getAutoParallel(),
getParentForChildren(),
isClean());
autoParallel_ = null;
}
return autoParallelBuilder_;
}
private org.tensorflow.framework.ScopedAllocatorOptions scopedAllocatorOpts_;
private org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.ScopedAllocatorOptions, org.tensorflow.framework.ScopedAllocatorOptions.Builder, org.tensorflow.framework.ScopedAllocatorOptionsOrBuilder> scopedAllocatorOptsBuilder_;
/**
* .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
*/
public boolean hasScopedAllocatorOpts() {
return scopedAllocatorOptsBuilder_ != null || scopedAllocatorOpts_ != null;
}
/**
* .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
*/
public org.tensorflow.framework.ScopedAllocatorOptions getScopedAllocatorOpts() {
if (scopedAllocatorOptsBuilder_ == null) {
return scopedAllocatorOpts_ == null ? org.tensorflow.framework.ScopedAllocatorOptions.getDefaultInstance() : scopedAllocatorOpts_;
} else {
return scopedAllocatorOptsBuilder_.getMessage();
}
}
/**
* .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
*/
public Builder setScopedAllocatorOpts(org.tensorflow.framework.ScopedAllocatorOptions value) {
if (scopedAllocatorOptsBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
scopedAllocatorOpts_ = value;
onChanged();
} else {
scopedAllocatorOptsBuilder_.setMessage(value);
}
return this;
}
/**
* .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
*/
public Builder setScopedAllocatorOpts(
org.tensorflow.framework.ScopedAllocatorOptions.Builder builderForValue) {
if (scopedAllocatorOptsBuilder_ == null) {
scopedAllocatorOpts_ = builderForValue.build();
onChanged();
} else {
scopedAllocatorOptsBuilder_.setMessage(builderForValue.build());
}
return this;
}
/**
* .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
*/
public Builder mergeScopedAllocatorOpts(org.tensorflow.framework.ScopedAllocatorOptions value) {
if (scopedAllocatorOptsBuilder_ == null) {
if (scopedAllocatorOpts_ != null) {
scopedAllocatorOpts_ =
org.tensorflow.framework.ScopedAllocatorOptions.newBuilder(scopedAllocatorOpts_).mergeFrom(value).buildPartial();
} else {
scopedAllocatorOpts_ = value;
}
onChanged();
} else {
scopedAllocatorOptsBuilder_.mergeFrom(value);
}
return this;
}
/**
* .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
*/
public Builder clearScopedAllocatorOpts() {
if (scopedAllocatorOptsBuilder_ == null) {
scopedAllocatorOpts_ = null;
onChanged();
} else {
scopedAllocatorOpts_ = null;
scopedAllocatorOptsBuilder_ = null;
}
return this;
}
/**
* .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
*/
public org.tensorflow.framework.ScopedAllocatorOptions.Builder getScopedAllocatorOptsBuilder() {
onChanged();
return getScopedAllocatorOptsFieldBuilder().getBuilder();
}
/**
* .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
*/
public org.tensorflow.framework.ScopedAllocatorOptionsOrBuilder getScopedAllocatorOptsOrBuilder() {
if (scopedAllocatorOptsBuilder_ != null) {
return scopedAllocatorOptsBuilder_.getMessageOrBuilder();
} else {
return scopedAllocatorOpts_ == null ?
org.tensorflow.framework.ScopedAllocatorOptions.getDefaultInstance() : scopedAllocatorOpts_;
}
}
/**
* .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
*/
private org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.ScopedAllocatorOptions, org.tensorflow.framework.ScopedAllocatorOptions.Builder, org.tensorflow.framework.ScopedAllocatorOptionsOrBuilder>
getScopedAllocatorOptsFieldBuilder() {
if (scopedAllocatorOptsBuilder_ == null) {
scopedAllocatorOptsBuilder_ = new org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.ScopedAllocatorOptions, org.tensorflow.framework.ScopedAllocatorOptions.Builder, org.tensorflow.framework.ScopedAllocatorOptionsOrBuilder>(
getScopedAllocatorOpts(),
getParentForChildren(),
isClean());
scopedAllocatorOpts_ = null;
}
return scopedAllocatorOptsBuilder_;
}
private org.nd4j.shade.protobuf.LazyStringList optimizers_ = org.nd4j.shade.protobuf.LazyStringArrayList.EMPTY;
private void ensureOptimizersIsMutable() {
if (!((bitField0_ & 0x00000001) != 0)) {
optimizers_ = new org.nd4j.shade.protobuf.LazyStringArrayList(optimizers_);
bitField0_ |= 0x00000001;
}
}
/**
*
* If non-empty, will use this as an alternative way to specify a list of
* optimizations to turn on and the order of the optimizations (replacing the
* meta-optimizer).
* Of the RewriterConfig options, only the AutoParallel configuration options
* (the auto_parallel field) apply to manually requested optimization passes
* ("autoparallel"). Memory optimization passes ("memory") invoked here are
* not configurable (in contrast to memory optimization passes through the
* meta-optimizer) and act only on manual op annotations.
* Custom registered optimizers will be run after the base optimizers, in
* the order that they are specified.
*
*
* repeated string optimizers = 100;
*/
public org.nd4j.shade.protobuf.ProtocolStringList
getOptimizersList() {
return optimizers_.getUnmodifiableView();
}
/**
*
* If non-empty, will use this as an alternative way to specify a list of
* optimizations to turn on and the order of the optimizations (replacing the
* meta-optimizer).
* Of the RewriterConfig options, only the AutoParallel configuration options
* (the auto_parallel field) apply to manually requested optimization passes
* ("autoparallel"). Memory optimization passes ("memory") invoked here are
* not configurable (in contrast to memory optimization passes through the
* meta-optimizer) and act only on manual op annotations.
* Custom registered optimizers will be run after the base optimizers, in
* the order that they are specified.
*
*
* repeated string optimizers = 100;
*/
public int getOptimizersCount() {
return optimizers_.size();
}
/**
*
* If non-empty, will use this as an alternative way to specify a list of
* optimizations to turn on and the order of the optimizations (replacing the
* meta-optimizer).
* Of the RewriterConfig options, only the AutoParallel configuration options
* (the auto_parallel field) apply to manually requested optimization passes
* ("autoparallel"). Memory optimization passes ("memory") invoked here are
* not configurable (in contrast to memory optimization passes through the
* meta-optimizer) and act only on manual op annotations.
* Custom registered optimizers will be run after the base optimizers, in
* the order that they are specified.
*
*
* repeated string optimizers = 100;
*/
public java.lang.String getOptimizers(int index) {
return optimizers_.get(index);
}
/**
*
* If non-empty, will use this as an alternative way to specify a list of
* optimizations to turn on and the order of the optimizations (replacing the
* meta-optimizer).
* Of the RewriterConfig options, only the AutoParallel configuration options
* (the auto_parallel field) apply to manually requested optimization passes
* ("autoparallel"). Memory optimization passes ("memory") invoked here are
* not configurable (in contrast to memory optimization passes through the
* meta-optimizer) and act only on manual op annotations.
* Custom registered optimizers will be run after the base optimizers, in
* the order that they are specified.
*
*
* repeated string optimizers = 100;
*/
public org.nd4j.shade.protobuf.ByteString
getOptimizersBytes(int index) {
return optimizers_.getByteString(index);
}
/**
*
* If non-empty, will use this as an alternative way to specify a list of
* optimizations to turn on and the order of the optimizations (replacing the
* meta-optimizer).
* Of the RewriterConfig options, only the AutoParallel configuration options
* (the auto_parallel field) apply to manually requested optimization passes
* ("autoparallel"). Memory optimization passes ("memory") invoked here are
* not configurable (in contrast to memory optimization passes through the
* meta-optimizer) and act only on manual op annotations.
* Custom registered optimizers will be run after the base optimizers, in
* the order that they are specified.
*
*
* repeated string optimizers = 100;
*/
public Builder setOptimizers(
int index, java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
ensureOptimizersIsMutable();
optimizers_.set(index, value);
onChanged();
return this;
}
/**
*
* If non-empty, will use this as an alternative way to specify a list of
* optimizations to turn on and the order of the optimizations (replacing the
* meta-optimizer).
* Of the RewriterConfig options, only the AutoParallel configuration options
* (the auto_parallel field) apply to manually requested optimization passes
* ("autoparallel"). Memory optimization passes ("memory") invoked here are
* not configurable (in contrast to memory optimization passes through the
* meta-optimizer) and act only on manual op annotations.
* Custom registered optimizers will be run after the base optimizers, in
* the order that they are specified.
*
*
* repeated string optimizers = 100;
*/
public Builder addOptimizers(
java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
ensureOptimizersIsMutable();
optimizers_.add(value);
onChanged();
return this;
}
/**
*
* If non-empty, will use this as an alternative way to specify a list of
* optimizations to turn on and the order of the optimizations (replacing the
* meta-optimizer).
* Of the RewriterConfig options, only the AutoParallel configuration options
* (the auto_parallel field) apply to manually requested optimization passes
* ("autoparallel"). Memory optimization passes ("memory") invoked here are
* not configurable (in contrast to memory optimization passes through the
* meta-optimizer) and act only on manual op annotations.
* Custom registered optimizers will be run after the base optimizers, in
* the order that they are specified.
*
*
* repeated string optimizers = 100;
*/
public Builder addAllOptimizers(
java.lang.Iterable values) {
ensureOptimizersIsMutable();
org.nd4j.shade.protobuf.AbstractMessageLite.Builder.addAll(
values, optimizers_);
onChanged();
return this;
}
/**
*
* If non-empty, will use this as an alternative way to specify a list of
* optimizations to turn on and the order of the optimizations (replacing the
* meta-optimizer).
* Of the RewriterConfig options, only the AutoParallel configuration options
* (the auto_parallel field) apply to manually requested optimization passes
* ("autoparallel"). Memory optimization passes ("memory") invoked here are
* not configurable (in contrast to memory optimization passes through the
* meta-optimizer) and act only on manual op annotations.
* Custom registered optimizers will be run after the base optimizers, in
* the order that they are specified.
*
*
* repeated string optimizers = 100;
*/
public Builder clearOptimizers() {
optimizers_ = org.nd4j.shade.protobuf.LazyStringArrayList.EMPTY;
bitField0_ = (bitField0_ & ~0x00000001);
onChanged();
return this;
}
/**
*
* If non-empty, will use this as an alternative way to specify a list of
* optimizations to turn on and the order of the optimizations (replacing the
* meta-optimizer).
* Of the RewriterConfig options, only the AutoParallel configuration options
* (the auto_parallel field) apply to manually requested optimization passes
* ("autoparallel"). Memory optimization passes ("memory") invoked here are
* not configurable (in contrast to memory optimization passes through the
* meta-optimizer) and act only on manual op annotations.
* Custom registered optimizers will be run after the base optimizers, in
* the order that they are specified.
*
*
* repeated string optimizers = 100;
*/
public Builder addOptimizersBytes(
org.nd4j.shade.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
checkByteStringIsUtf8(value);
ensureOptimizersIsMutable();
optimizers_.add(value);
onChanged();
return this;
}
private java.util.List customOptimizers_ =
java.util.Collections.emptyList();
private void ensureCustomOptimizersIsMutable() {
if (!((bitField0_ & 0x00000002) != 0)) {
customOptimizers_ = new java.util.ArrayList(customOptimizers_);
bitField0_ |= 0x00000002;
}
}
private org.nd4j.shade.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizerOrBuilder> customOptimizersBuilder_;
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public java.util.List getCustomOptimizersList() {
if (customOptimizersBuilder_ == null) {
return java.util.Collections.unmodifiableList(customOptimizers_);
} else {
return customOptimizersBuilder_.getMessageList();
}
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public int getCustomOptimizersCount() {
if (customOptimizersBuilder_ == null) {
return customOptimizers_.size();
} else {
return customOptimizersBuilder_.getCount();
}
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer getCustomOptimizers(int index) {
if (customOptimizersBuilder_ == null) {
return customOptimizers_.get(index);
} else {
return customOptimizersBuilder_.getMessage(index);
}
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public Builder setCustomOptimizers(
int index, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer value) {
if (customOptimizersBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureCustomOptimizersIsMutable();
customOptimizers_.set(index, value);
onChanged();
} else {
customOptimizersBuilder_.setMessage(index, value);
}
return this;
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public Builder setCustomOptimizers(
int index, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder builderForValue) {
if (customOptimizersBuilder_ == null) {
ensureCustomOptimizersIsMutable();
customOptimizers_.set(index, builderForValue.build());
onChanged();
} else {
customOptimizersBuilder_.setMessage(index, builderForValue.build());
}
return this;
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public Builder addCustomOptimizers(org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer value) {
if (customOptimizersBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureCustomOptimizersIsMutable();
customOptimizers_.add(value);
onChanged();
} else {
customOptimizersBuilder_.addMessage(value);
}
return this;
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public Builder addCustomOptimizers(
int index, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer value) {
if (customOptimizersBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureCustomOptimizersIsMutable();
customOptimizers_.add(index, value);
onChanged();
} else {
customOptimizersBuilder_.addMessage(index, value);
}
return this;
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public Builder addCustomOptimizers(
org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder builderForValue) {
if (customOptimizersBuilder_ == null) {
ensureCustomOptimizersIsMutable();
customOptimizers_.add(builderForValue.build());
onChanged();
} else {
customOptimizersBuilder_.addMessage(builderForValue.build());
}
return this;
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public Builder addCustomOptimizers(
int index, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder builderForValue) {
if (customOptimizersBuilder_ == null) {
ensureCustomOptimizersIsMutable();
customOptimizers_.add(index, builderForValue.build());
onChanged();
} else {
customOptimizersBuilder_.addMessage(index, builderForValue.build());
}
return this;
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public Builder addAllCustomOptimizers(
java.lang.Iterable values) {
if (customOptimizersBuilder_ == null) {
ensureCustomOptimizersIsMutable();
org.nd4j.shade.protobuf.AbstractMessageLite.Builder.addAll(
values, customOptimizers_);
onChanged();
} else {
customOptimizersBuilder_.addAllMessages(values);
}
return this;
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public Builder clearCustomOptimizers() {
if (customOptimizersBuilder_ == null) {
customOptimizers_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000002);
onChanged();
} else {
customOptimizersBuilder_.clear();
}
return this;
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public Builder removeCustomOptimizers(int index) {
if (customOptimizersBuilder_ == null) {
ensureCustomOptimizersIsMutable();
customOptimizers_.remove(index);
onChanged();
} else {
customOptimizersBuilder_.remove(index);
}
return this;
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder getCustomOptimizersBuilder(
int index) {
return getCustomOptimizersFieldBuilder().getBuilder(index);
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizerOrBuilder getCustomOptimizersOrBuilder(
int index) {
if (customOptimizersBuilder_ == null) {
return customOptimizers_.get(index); } else {
return customOptimizersBuilder_.getMessageOrBuilder(index);
}
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public java.util.List
getCustomOptimizersOrBuilderList() {
if (customOptimizersBuilder_ != null) {
return customOptimizersBuilder_.getMessageOrBuilderList();
} else {
return java.util.Collections.unmodifiableList(customOptimizers_);
}
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder addCustomOptimizersBuilder() {
return getCustomOptimizersFieldBuilder().addBuilder(
org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.getDefaultInstance());
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder addCustomOptimizersBuilder(
int index) {
return getCustomOptimizersFieldBuilder().addBuilder(
index, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.getDefaultInstance());
}
/**
*
* list of CustomGraphOptimizers to apply.
*
*
* repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;
*/
public java.util.List
getCustomOptimizersBuilderList() {
return getCustomOptimizersFieldBuilder().getBuilderList();
}
private org.nd4j.shade.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizerOrBuilder>
getCustomOptimizersFieldBuilder() {
if (customOptimizersBuilder_ == null) {
customOptimizersBuilder_ = new org.nd4j.shade.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizer.Builder, org.tensorflow.framework.RewriterConfig.CustomGraphOptimizerOrBuilder>(
customOptimizers_,
((bitField0_ & 0x00000002) != 0),
getParentForChildren(),
isClean());
customOptimizers_ = null;
}
return customOptimizersBuilder_;
}
@java.lang.Override
public final Builder setUnknownFields(
final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:tensorflow.RewriterConfig)
}
// @@protoc_insertion_point(class_scope:tensorflow.RewriterConfig)
private static final org.tensorflow.framework.RewriterConfig DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new org.tensorflow.framework.RewriterConfig();
}
public static org.tensorflow.framework.RewriterConfig getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final org.nd4j.shade.protobuf.Parser
PARSER = new org.nd4j.shade.protobuf.AbstractParser() {
@java.lang.Override
public RewriterConfig parsePartialFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return new RewriterConfig(input, extensionRegistry);
}
};
public static org.nd4j.shade.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public org.nd4j.shade.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public org.tensorflow.framework.RewriterConfig getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}