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// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/contrib/lite/toco/toco_flags.proto
package toco;
public final class TocoFlagsOuterClass {
private TocoFlagsOuterClass() {}
public static void registerAllExtensions(
com.google.protobuf.ExtensionRegistryLite registry) {
}
public static void registerAllExtensions(
com.google.protobuf.ExtensionRegistry registry) {
registerAllExtensions(
(com.google.protobuf.ExtensionRegistryLite) registry);
}
/**
*
* Supported I/O file formats. Some formats may be input-only or output-only.
*
*
* Protobuf enum {@code toco.FileFormat}
*/
public enum FileFormat
implements com.google.protobuf.ProtocolMessageEnum {
/**
* FILE_FORMAT_UNKNOWN = 0;
*/
FILE_FORMAT_UNKNOWN(0),
/**
*
* GraphDef, third_party/tensorflow/core/framework/graph.proto
*
*
* TENSORFLOW_GRAPHDEF = 1;
*/
TENSORFLOW_GRAPHDEF(1),
/**
*
* Tensorflow's mobile inference model.
* third_party/tensorflow/contrib/tflite/schema.fbs
*
*
* TFLITE = 2;
*/
TFLITE(2),
/**
*
* GraphViz
* Export-only.
*
*
* GRAPHVIZ_DOT = 3;
*/
GRAPHVIZ_DOT(3),
;
/**
* FILE_FORMAT_UNKNOWN = 0;
*/
public static final int FILE_FORMAT_UNKNOWN_VALUE = 0;
/**
*
* GraphDef, third_party/tensorflow/core/framework/graph.proto
*
*
* TENSORFLOW_GRAPHDEF = 1;
*/
public static final int TENSORFLOW_GRAPHDEF_VALUE = 1;
/**
*
* Tensorflow's mobile inference model.
* third_party/tensorflow/contrib/tflite/schema.fbs
*
*
* TFLITE = 2;
*/
public static final int TFLITE_VALUE = 2;
/**
*
* GraphViz
* Export-only.
*
*
* GRAPHVIZ_DOT = 3;
*/
public static final int GRAPHVIZ_DOT_VALUE = 3;
public final int getNumber() {
return value;
}
/**
* @deprecated Use {@link #forNumber(int)} instead.
*/
@java.lang.Deprecated
public static FileFormat valueOf(int value) {
return forNumber(value);
}
public static FileFormat forNumber(int value) {
switch (value) {
case 0: return FILE_FORMAT_UNKNOWN;
case 1: return TENSORFLOW_GRAPHDEF;
case 2: return TFLITE;
case 3: return GRAPHVIZ_DOT;
default: return null;
}
}
public static com.google.protobuf.Internal.EnumLiteMap
internalGetValueMap() {
return internalValueMap;
}
private static final com.google.protobuf.Internal.EnumLiteMap<
FileFormat> internalValueMap =
new com.google.protobuf.Internal.EnumLiteMap() {
public FileFormat findValueByNumber(int number) {
return FileFormat.forNumber(number);
}
};
public final com.google.protobuf.Descriptors.EnumValueDescriptor
getValueDescriptor() {
return getDescriptor().getValues().get(ordinal());
}
public final com.google.protobuf.Descriptors.EnumDescriptor
getDescriptorForType() {
return getDescriptor();
}
public static final com.google.protobuf.Descriptors.EnumDescriptor
getDescriptor() {
return toco.TocoFlagsOuterClass.getDescriptor().getEnumTypes().get(0);
}
private static final FileFormat[] VALUES = values();
public static FileFormat valueOf(
com.google.protobuf.Descriptors.EnumValueDescriptor desc) {
if (desc.getType() != getDescriptor()) {
throw new java.lang.IllegalArgumentException(
"EnumValueDescriptor is not for this type.");
}
return VALUES[desc.getIndex()];
}
private final int value;
private FileFormat(int value) {
this.value = value;
}
// @@protoc_insertion_point(enum_scope:toco.FileFormat)
}
public interface TocoFlagsOrBuilder extends
// @@protoc_insertion_point(interface_extends:toco.TocoFlags)
com.google.protobuf.MessageOrBuilder {
/**
*
* Input file format
*
*
* optional .toco.FileFormat input_format = 1;
*/
boolean hasInputFormat();
/**
*
* Input file format
*
*
* optional .toco.FileFormat input_format = 1;
*/
toco.TocoFlagsOuterClass.FileFormat getInputFormat();
/**
*
* Output file format
*
*
* optional .toco.FileFormat output_format = 2;
*/
boolean hasOutputFormat();
/**
*
* Output file format
*
*
* optional .toco.FileFormat output_format = 2;
*/
toco.TocoFlagsOuterClass.FileFormat getOutputFormat();
/**
*
* Similar to inference_type, but allows to control specifically the
* quantization of input arrays, separately from other arrays.
* If not set, then the value of inference_type is implicitly used, i.e.
* by default input arrays are quantized like other arrays.
* Like inference_type, this only affects real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* The typical use for this flag is for vision models taking a bitmap
* as input, typically with uint8 channels, yet still requiring floating-point
* inference. For such image models, the uint8 input is quantized, i.e.
* the uint8 values are interpreted as real numbers, and the quantization
* parameters used for such input arrays are their mean_value, std_value
* parameters.
*
*
* optional .toco.IODataType inference_input_type = 11;
*/
boolean hasInferenceInputType();
/**
*
* Similar to inference_type, but allows to control specifically the
* quantization of input arrays, separately from other arrays.
* If not set, then the value of inference_type is implicitly used, i.e.
* by default input arrays are quantized like other arrays.
* Like inference_type, this only affects real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* The typical use for this flag is for vision models taking a bitmap
* as input, typically with uint8 channels, yet still requiring floating-point
* inference. For such image models, the uint8 input is quantized, i.e.
* the uint8 values are interpreted as real numbers, and the quantization
* parameters used for such input arrays are their mean_value, std_value
* parameters.
*
*
* optional .toco.IODataType inference_input_type = 11;
*/
toco.Types.IODataType getInferenceInputType();
/**
*
* Sets the type of real-number arrays in the output file, that is, controls
* the representation (quantization) of real numbers in the output file,
* except for input arrays, which are controlled by inference_input_type.
* NOTE: this flag only impacts real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* For real-number arrays, the impact of this flag is to allow the output
* file to choose a different real-numbers representation (quantization)
* from what the input file used. For any other types of arrays, changing
* the data type would not make sense.
* Specifically:
* - If FLOAT, then real-numbers arrays will be of type float in
* the output file. If they were quantized in the input file, then
* they get dequantized.
* - If QUANTIZED_UINT8, then real-numbers arrays will be quantized
* as uint8 in the output file. If they were float in the input file,
* then they get quantized.
* - If not set, then all real-numbers arrays retain the same type in the
* output file as they have in the input file.
*
*
* optional .toco.IODataType inference_type = 4;
*/
boolean hasInferenceType();
/**
*
* Sets the type of real-number arrays in the output file, that is, controls
* the representation (quantization) of real numbers in the output file,
* except for input arrays, which are controlled by inference_input_type.
* NOTE: this flag only impacts real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* For real-number arrays, the impact of this flag is to allow the output
* file to choose a different real-numbers representation (quantization)
* from what the input file used. For any other types of arrays, changing
* the data type would not make sense.
* Specifically:
* - If FLOAT, then real-numbers arrays will be of type float in
* the output file. If they were quantized in the input file, then
* they get dequantized.
* - If QUANTIZED_UINT8, then real-numbers arrays will be quantized
* as uint8 in the output file. If they were float in the input file,
* then they get quantized.
* - If not set, then all real-numbers arrays retain the same type in the
* output file as they have in the input file.
*
*
* optional .toco.IODataType inference_type = 4;
*/
toco.Types.IODataType getInferenceType();
/**
*
* default_ranges_min and default_ranges_max are helpers to experiment
* with quantization of models. Normally, quantization requires the input
* model to have (min, max) range information for every activations array.
* This is needed in order to know how to quantize arrays and still achieve
* satisfactory accuracy. However, in some circumstances one would just like
* to estimate the performance of quantized inference, without caring about
* accuracy. That is what default_ranges_min and default_ranges_max are for:
* when specified, they will be used as default (min, max) range boundaries
* for all activation arrays that lack (min, max) range information, thus
* allowing for quantization to proceed.
* It should be clear from the above explanation that these parameters are
* for experimentation purposes only and should not be used in production:
* they make it easy to quantize models, but the resulting quantized model
* will be inaccurate.
* These values only apply to arrays quantized with the kUint8 data type.
*
*
* optional float default_ranges_min = 5;
*/
boolean hasDefaultRangesMin();
/**
*
* default_ranges_min and default_ranges_max are helpers to experiment
* with quantization of models. Normally, quantization requires the input
* model to have (min, max) range information for every activations array.
* This is needed in order to know how to quantize arrays and still achieve
* satisfactory accuracy. However, in some circumstances one would just like
* to estimate the performance of quantized inference, without caring about
* accuracy. That is what default_ranges_min and default_ranges_max are for:
* when specified, they will be used as default (min, max) range boundaries
* for all activation arrays that lack (min, max) range information, thus
* allowing for quantization to proceed.
* It should be clear from the above explanation that these parameters are
* for experimentation purposes only and should not be used in production:
* they make it easy to quantize models, but the resulting quantized model
* will be inaccurate.
* These values only apply to arrays quantized with the kUint8 data type.
*
*
* optional float default_ranges_min = 5;
*/
float getDefaultRangesMin();
/**
* optional float default_ranges_max = 6;
*/
boolean hasDefaultRangesMax();
/**
* optional float default_ranges_max = 6;
*/
float getDefaultRangesMax();
/**
*
* Equivalent versions of default_ranges_min/_max for arrays quantized with
* the kInt16 data type.
*
*
* optional float default_int16_ranges_min = 15;
*/
boolean hasDefaultInt16RangesMin();
/**
*
* Equivalent versions of default_ranges_min/_max for arrays quantized with
* the kInt16 data type.
*
*
* optional float default_int16_ranges_min = 15;
*/
float getDefaultInt16RangesMin();
/**
* optional float default_int16_ranges_max = 16;
*/
boolean hasDefaultInt16RangesMax();
/**
* optional float default_int16_ranges_max = 16;
*/
float getDefaultInt16RangesMax();
/**
*
* Ignore and discard FakeQuant nodes. For instance, that can be used to
* generate plain float code without fake-quantization from a quantized
* graph.
*
*
* optional bool drop_fake_quant = 7;
*/
boolean hasDropFakeQuant();
/**
*
* Ignore and discard FakeQuant nodes. For instance, that can be used to
* generate plain float code without fake-quantization from a quantized
* graph.
*
*
* optional bool drop_fake_quant = 7;
*/
boolean getDropFakeQuant();
/**
*
* Normally, FakeQuant nodes must be strict boundaries for graph
* transformations, in order to ensure that quantized inference has the
* exact same arithmetic behavior as quantized training --- which is the
* whole point of quantized training and of FakeQuant nodes in the first
* place. However, that entails subtle requirements on where exactly
* FakeQuant nodes must be placed in the graph. Some quantized graphs
* have FakeQuant nodes at unexpected locations, that prevent graph
* transformations that are necessary in order to generate inference
* code for these graphs. Such graphs should be fixed, but as a
* temporary work-around, setting this reorder_across_fake_quant flag
* allows toco to perform necessary graph transformations on them,
* at the cost of no longer faithfully matching inference and training
* arithmetic.
*
*
* optional bool reorder_across_fake_quant = 8;
*/
boolean hasReorderAcrossFakeQuant();
/**
*
* Normally, FakeQuant nodes must be strict boundaries for graph
* transformations, in order to ensure that quantized inference has the
* exact same arithmetic behavior as quantized training --- which is the
* whole point of quantized training and of FakeQuant nodes in the first
* place. However, that entails subtle requirements on where exactly
* FakeQuant nodes must be placed in the graph. Some quantized graphs
* have FakeQuant nodes at unexpected locations, that prevent graph
* transformations that are necessary in order to generate inference
* code for these graphs. Such graphs should be fixed, but as a
* temporary work-around, setting this reorder_across_fake_quant flag
* allows toco to perform necessary graph transformations on them,
* at the cost of no longer faithfully matching inference and training
* arithmetic.
*
*
* optional bool reorder_across_fake_quant = 8;
*/
boolean getReorderAcrossFakeQuant();
/**
*
* If true, allow TOCO to create TF Lite Custom operators for all the
* unsupported Tensorflow ops.
*
*
* optional bool allow_custom_ops = 10;
*/
boolean hasAllowCustomOps();
/**
*
* If true, allow TOCO to create TF Lite Custom operators for all the
* unsupported Tensorflow ops.
*
*
* optional bool allow_custom_ops = 10;
*/
boolean getAllowCustomOps();
/**
*
* Applies only to the case when the input format is TENSORFLOW_GRAPHDEF.
* If true, then control dependencies will be immediately dropped during
* import.
* If not set, the default behavior is as follows:
* - Default to false if the output format is TENSORFLOW_GRAPHDEF.
* - Default to true in all other cases.
*
*
* optional bool drop_control_dependency = 12;
*/
boolean hasDropControlDependency();
/**
*
* Applies only to the case when the input format is TENSORFLOW_GRAPHDEF.
* If true, then control dependencies will be immediately dropped during
* import.
* If not set, the default behavior is as follows:
* - Default to false if the output format is TENSORFLOW_GRAPHDEF.
* - Default to true in all other cases.
*
*
* optional bool drop_control_dependency = 12;
*/
boolean getDropControlDependency();
/**
*
* Disables transformations that fuse subgraphs such as known LSTMs (not all
* LSTMs are identified).
*
*
* optional bool debug_disable_recurrent_cell_fusion = 13;
*/
boolean hasDebugDisableRecurrentCellFusion();
/**
*
* Disables transformations that fuse subgraphs such as known LSTMs (not all
* LSTMs are identified).
*
*
* optional bool debug_disable_recurrent_cell_fusion = 13;
*/
boolean getDebugDisableRecurrentCellFusion();
/**
*
* Uses the FakeQuantWithMinMaxArgs.num_bits attribute to adjust quantized
* array data types throughout the graph. The graph must be properly annotated
* with FakeQuant* ops on at least the edges and may contain additional ops on
* the interior of the graph to widen/narrow as desired.
* Input and output array data types may change because of this propagation
* and users must be sure to query the final data_type values.
*
*
* optional bool propagate_fake_quant_num_bits = 14;
*/
boolean hasPropagateFakeQuantNumBits();
/**
*
* Uses the FakeQuantWithMinMaxArgs.num_bits attribute to adjust quantized
* array data types throughout the graph. The graph must be properly annotated
* with FakeQuant* ops on at least the edges and may contain additional ops on
* the interior of the graph to widen/narrow as desired.
* Input and output array data types may change because of this propagation
* and users must be sure to query the final data_type values.
*
*
* optional bool propagate_fake_quant_num_bits = 14;
*/
boolean getPropagateFakeQuantNumBits();
/**
*
* Some fast uint8 GEMM kernels require uint8 weights to avoid the value 0.
* This flag allows nudging them to 1 to allow proceeding, with moderate
* inaccuracy.
*
*
* optional bool allow_nudging_weights_to_use_fast_gemm_kernel = 17;
*/
boolean hasAllowNudgingWeightsToUseFastGemmKernel();
/**
*
* Some fast uint8 GEMM kernels require uint8 weights to avoid the value 0.
* This flag allows nudging them to 1 to allow proceeding, with moderate
* inaccuracy.
*
*
* optional bool allow_nudging_weights_to_use_fast_gemm_kernel = 17;
*/
boolean getAllowNudgingWeightsToUseFastGemmKernel();
/**
*
* Minimum size of constant arrays to deduplicate; arrays smaller will not be
* deduplicated.
*
*
* optional int64 dedupe_array_min_size_bytes = 18 [default = 64];
*/
boolean hasDedupeArrayMinSizeBytes();
/**
*
* Minimum size of constant arrays to deduplicate; arrays smaller will not be
* deduplicated.
*
*
* optional int64 dedupe_array_min_size_bytes = 18 [default = 64];
*/
long getDedupeArrayMinSizeBytes();
/**
*
* Split the LSTM inputs from 5 tensors to 18 tensors for TFLite.
* Ignored if the output format is not TFLite.
*
*
* optional bool split_tflite_lstm_inputs = 19 [default = true];
*/
boolean hasSplitTfliteLstmInputs();
/**
*
* Split the LSTM inputs from 5 tensors to 18 tensors for TFLite.
* Ignored if the output format is not TFLite.
*
*
* optional bool split_tflite_lstm_inputs = 19 [default = true];
*/
boolean getSplitTfliteLstmInputs();
/**
*
* Store weights as quantized weights followed by dequantize operations.
* Computation is still done in float, but reduces model size (at the cost of
* accuracy and latency).
* DEPRECATED: Please use post_training_quantize instead.
*
*
* optional bool quantize_weights = 20 [default = false];
*/
boolean hasQuantizeWeights();
/**
*
* Store weights as quantized weights followed by dequantize operations.
* Computation is still done in float, but reduces model size (at the cost of
* accuracy and latency).
* DEPRECATED: Please use post_training_quantize instead.
*
*
* optional bool quantize_weights = 20 [default = false];
*/
boolean getQuantizeWeights();
/**
*
* Full filepath of folder to dump the graphs at various stages of processing
* GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order
* to keep the requirements of the output file.
*
*
* optional string dump_graphviz_dir = 24;
*/
boolean hasDumpGraphvizDir();
/**
*
* Full filepath of folder to dump the graphs at various stages of processing
* GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order
* to keep the requirements of the output file.
*
*
* optional string dump_graphviz_dir = 24;
*/
java.lang.String getDumpGraphvizDir();
/**
*
* Full filepath of folder to dump the graphs at various stages of processing
* GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order
* to keep the requirements of the output file.
*
*
* optional string dump_graphviz_dir = 24;
*/
com.google.protobuf.ByteString
getDumpGraphvizDirBytes();
/**
*
* Boolean indicating whether to dump the graph after every graph
* transformation.
*
*
* optional bool dump_graphviz_include_video = 25;
*/
boolean hasDumpGraphvizIncludeVideo();
/**
*
* Boolean indicating whether to dump the graph after every graph
* transformation.
*
*
* optional bool dump_graphviz_include_video = 25;
*/
boolean getDumpGraphvizIncludeVideo();
/**
*
* Boolean indicating whether to quantize the weights of the converted float
* model. Model size will be reduced and there will be latency improvements
* (at the cost of accuracy).
*
*
* optional bool post_training_quantize = 26 [default = false];
*/
boolean hasPostTrainingQuantize();
/**
*
* Boolean indicating whether to quantize the weights of the converted float
* model. Model size will be reduced and there will be latency improvements
* (at the cost of accuracy).
*
*
* optional bool post_training_quantize = 26 [default = false];
*/
boolean getPostTrainingQuantize();
/**
*
* When enabled, unsupported ops will be converted to TFLite Flex ops.
* TODO(ycling): Consider to rename the following 2 flags and don't call it
* "Flex".
* `allow_flex_ops` should always be used with `allow_custom_ops`.
* WARNING: Experimental interface, subject to change
*
*
* optional bool allow_flex_ops = 27 [default = false];
*/
boolean hasAllowFlexOps();
/**
*
* When enabled, unsupported ops will be converted to TFLite Flex ops.
* TODO(ycling): Consider to rename the following 2 flags and don't call it
* "Flex".
* `allow_flex_ops` should always be used with `allow_custom_ops`.
* WARNING: Experimental interface, subject to change
*
*
* optional bool allow_flex_ops = 27 [default = false];
*/
boolean getAllowFlexOps();
/**
*
* When enabled, all TensorFlow ops will be converted to TFLite Flex
* ops directly. This will force `allow_flex_ops` to true.
* `force_flex_ops` should always be used with `allow_flex_ops`.
* WARNING: Experimental interface, subject to change
*
*
* optional bool force_flex_ops = 28 [default = false];
*/
boolean hasForceFlexOps();
/**
*
* When enabled, all TensorFlow ops will be converted to TFLite Flex
* ops directly. This will force `allow_flex_ops` to true.
* `force_flex_ops` should always be used with `allow_flex_ops`.
* WARNING: Experimental interface, subject to change
*
*
* optional bool force_flex_ops = 28 [default = false];
*/
boolean getForceFlexOps();
}
/**
*
* TocoFlags encodes extra parameters that drive tooling operations, that
* are not normally encoded in model files and in general may not be thought
* of as properties of models, instead describing how models are to be
* processed in the context of the present tooling job.
* Next ID to use: 29.
*
*
* Protobuf type {@code toco.TocoFlags}
*/
public static final class TocoFlags extends
com.google.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:toco.TocoFlags)
TocoFlagsOrBuilder {
private static final long serialVersionUID = 0L;
// Use TocoFlags.newBuilder() to construct.
private TocoFlags(com.google.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private TocoFlags() {
inputFormat_ = 0;
outputFormat_ = 0;
inferenceInputType_ = 0;
inferenceType_ = 0;
defaultRangesMin_ = 0F;
defaultRangesMax_ = 0F;
defaultInt16RangesMin_ = 0F;
defaultInt16RangesMax_ = 0F;
dropFakeQuant_ = false;
reorderAcrossFakeQuant_ = false;
allowCustomOps_ = false;
dropControlDependency_ = false;
debugDisableRecurrentCellFusion_ = false;
propagateFakeQuantNumBits_ = false;
allowNudgingWeightsToUseFastGemmKernel_ = false;
dedupeArrayMinSizeBytes_ = 64L;
splitTfliteLstmInputs_ = true;
quantizeWeights_ = false;
dumpGraphvizDir_ = "";
dumpGraphvizIncludeVideo_ = false;
postTrainingQuantize_ = false;
allowFlexOps_ = false;
forceFlexOps_ = false;
}
@java.lang.Override
public final com.google.protobuf.UnknownFieldSet
getUnknownFields() {
return this.unknownFields;
}
private TocoFlags(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
this();
if (extensionRegistry == null) {
throw new java.lang.NullPointerException();
}
int mutable_bitField0_ = 0;
com.google.protobuf.UnknownFieldSet.Builder unknownFields =
com.google.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();
@SuppressWarnings("deprecation")
toco.TocoFlagsOuterClass.FileFormat value = toco.TocoFlagsOuterClass.FileFormat.valueOf(rawValue);
if (value == null) {
unknownFields.mergeVarintField(1, rawValue);
} else {
bitField0_ |= 0x00000001;
inputFormat_ = rawValue;
}
break;
}
case 16: {
int rawValue = input.readEnum();
@SuppressWarnings("deprecation")
toco.TocoFlagsOuterClass.FileFormat value = toco.TocoFlagsOuterClass.FileFormat.valueOf(rawValue);
if (value == null) {
unknownFields.mergeVarintField(2, rawValue);
} else {
bitField0_ |= 0x00000002;
outputFormat_ = rawValue;
}
break;
}
case 32: {
int rawValue = input.readEnum();
@SuppressWarnings("deprecation")
toco.Types.IODataType value = toco.Types.IODataType.valueOf(rawValue);
if (value == null) {
unknownFields.mergeVarintField(4, rawValue);
} else {
bitField0_ |= 0x00000008;
inferenceType_ = rawValue;
}
break;
}
case 45: {
bitField0_ |= 0x00000010;
defaultRangesMin_ = input.readFloat();
break;
}
case 53: {
bitField0_ |= 0x00000020;
defaultRangesMax_ = input.readFloat();
break;
}
case 56: {
bitField0_ |= 0x00000100;
dropFakeQuant_ = input.readBool();
break;
}
case 64: {
bitField0_ |= 0x00000200;
reorderAcrossFakeQuant_ = input.readBool();
break;
}
case 80: {
bitField0_ |= 0x00000400;
allowCustomOps_ = input.readBool();
break;
}
case 88: {
int rawValue = input.readEnum();
@SuppressWarnings("deprecation")
toco.Types.IODataType value = toco.Types.IODataType.valueOf(rawValue);
if (value == null) {
unknownFields.mergeVarintField(11, rawValue);
} else {
bitField0_ |= 0x00000004;
inferenceInputType_ = rawValue;
}
break;
}
case 96: {
bitField0_ |= 0x00000800;
dropControlDependency_ = input.readBool();
break;
}
case 104: {
bitField0_ |= 0x00001000;
debugDisableRecurrentCellFusion_ = input.readBool();
break;
}
case 112: {
bitField0_ |= 0x00002000;
propagateFakeQuantNumBits_ = input.readBool();
break;
}
case 125: {
bitField0_ |= 0x00000040;
defaultInt16RangesMin_ = input.readFloat();
break;
}
case 133: {
bitField0_ |= 0x00000080;
defaultInt16RangesMax_ = input.readFloat();
break;
}
case 136: {
bitField0_ |= 0x00004000;
allowNudgingWeightsToUseFastGemmKernel_ = input.readBool();
break;
}
case 144: {
bitField0_ |= 0x00008000;
dedupeArrayMinSizeBytes_ = input.readInt64();
break;
}
case 152: {
bitField0_ |= 0x00010000;
splitTfliteLstmInputs_ = input.readBool();
break;
}
case 160: {
bitField0_ |= 0x00020000;
quantizeWeights_ = input.readBool();
break;
}
case 194: {
com.google.protobuf.ByteString bs = input.readBytes();
bitField0_ |= 0x00040000;
dumpGraphvizDir_ = bs;
break;
}
case 200: {
bitField0_ |= 0x00080000;
dumpGraphvizIncludeVideo_ = input.readBool();
break;
}
case 208: {
bitField0_ |= 0x00100000;
postTrainingQuantize_ = input.readBool();
break;
}
case 216: {
bitField0_ |= 0x00200000;
allowFlexOps_ = input.readBool();
break;
}
case 224: {
bitField0_ |= 0x00400000;
forceFlexOps_ = input.readBool();
break;
}
default: {
if (!parseUnknownField(
input, unknownFields, extensionRegistry, tag)) {
done = true;
}
break;
}
}
}
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.setUnfinishedMessage(this);
} catch (java.io.IOException e) {
throw new com.google.protobuf.InvalidProtocolBufferException(
e).setUnfinishedMessage(this);
} finally {
this.unknownFields = unknownFields.build();
makeExtensionsImmutable();
}
}
public static final com.google.protobuf.Descriptors.Descriptor
getDescriptor() {
return toco.TocoFlagsOuterClass.internal_static_toco_TocoFlags_descriptor;
}
@java.lang.Override
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return toco.TocoFlagsOuterClass.internal_static_toco_TocoFlags_fieldAccessorTable
.ensureFieldAccessorsInitialized(
toco.TocoFlagsOuterClass.TocoFlags.class, toco.TocoFlagsOuterClass.TocoFlags.Builder.class);
}
private int bitField0_;
public static final int INPUT_FORMAT_FIELD_NUMBER = 1;
private int inputFormat_;
/**
*
* Input file format
*
*
* optional .toco.FileFormat input_format = 1;
*/
public boolean hasInputFormat() {
return ((bitField0_ & 0x00000001) == 0x00000001);
}
/**
*
* Input file format
*
*
* optional .toco.FileFormat input_format = 1;
*/
public toco.TocoFlagsOuterClass.FileFormat getInputFormat() {
@SuppressWarnings("deprecation")
toco.TocoFlagsOuterClass.FileFormat result = toco.TocoFlagsOuterClass.FileFormat.valueOf(inputFormat_);
return result == null ? toco.TocoFlagsOuterClass.FileFormat.FILE_FORMAT_UNKNOWN : result;
}
public static final int OUTPUT_FORMAT_FIELD_NUMBER = 2;
private int outputFormat_;
/**
*
* Output file format
*
*
* optional .toco.FileFormat output_format = 2;
*/
public boolean hasOutputFormat() {
return ((bitField0_ & 0x00000002) == 0x00000002);
}
/**
*
* Output file format
*
*
* optional .toco.FileFormat output_format = 2;
*/
public toco.TocoFlagsOuterClass.FileFormat getOutputFormat() {
@SuppressWarnings("deprecation")
toco.TocoFlagsOuterClass.FileFormat result = toco.TocoFlagsOuterClass.FileFormat.valueOf(outputFormat_);
return result == null ? toco.TocoFlagsOuterClass.FileFormat.FILE_FORMAT_UNKNOWN : result;
}
public static final int INFERENCE_INPUT_TYPE_FIELD_NUMBER = 11;
private int inferenceInputType_;
/**
*
* Similar to inference_type, but allows to control specifically the
* quantization of input arrays, separately from other arrays.
* If not set, then the value of inference_type is implicitly used, i.e.
* by default input arrays are quantized like other arrays.
* Like inference_type, this only affects real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* The typical use for this flag is for vision models taking a bitmap
* as input, typically with uint8 channels, yet still requiring floating-point
* inference. For such image models, the uint8 input is quantized, i.e.
* the uint8 values are interpreted as real numbers, and the quantization
* parameters used for such input arrays are their mean_value, std_value
* parameters.
*
*
* optional .toco.IODataType inference_input_type = 11;
*/
public boolean hasInferenceInputType() {
return ((bitField0_ & 0x00000004) == 0x00000004);
}
/**
*
* Similar to inference_type, but allows to control specifically the
* quantization of input arrays, separately from other arrays.
* If not set, then the value of inference_type is implicitly used, i.e.
* by default input arrays are quantized like other arrays.
* Like inference_type, this only affects real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* The typical use for this flag is for vision models taking a bitmap
* as input, typically with uint8 channels, yet still requiring floating-point
* inference. For such image models, the uint8 input is quantized, i.e.
* the uint8 values are interpreted as real numbers, and the quantization
* parameters used for such input arrays are their mean_value, std_value
* parameters.
*
*
* optional .toco.IODataType inference_input_type = 11;
*/
public toco.Types.IODataType getInferenceInputType() {
@SuppressWarnings("deprecation")
toco.Types.IODataType result = toco.Types.IODataType.valueOf(inferenceInputType_);
return result == null ? toco.Types.IODataType.IO_DATA_TYPE_UNKNOWN : result;
}
public static final int INFERENCE_TYPE_FIELD_NUMBER = 4;
private int inferenceType_;
/**
*
* Sets the type of real-number arrays in the output file, that is, controls
* the representation (quantization) of real numbers in the output file,
* except for input arrays, which are controlled by inference_input_type.
* NOTE: this flag only impacts real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* For real-number arrays, the impact of this flag is to allow the output
* file to choose a different real-numbers representation (quantization)
* from what the input file used. For any other types of arrays, changing
* the data type would not make sense.
* Specifically:
* - If FLOAT, then real-numbers arrays will be of type float in
* the output file. If they were quantized in the input file, then
* they get dequantized.
* - If QUANTIZED_UINT8, then real-numbers arrays will be quantized
* as uint8 in the output file. If they were float in the input file,
* then they get quantized.
* - If not set, then all real-numbers arrays retain the same type in the
* output file as they have in the input file.
*
*
* optional .toco.IODataType inference_type = 4;
*/
public boolean hasInferenceType() {
return ((bitField0_ & 0x00000008) == 0x00000008);
}
/**
*
* Sets the type of real-number arrays in the output file, that is, controls
* the representation (quantization) of real numbers in the output file,
* except for input arrays, which are controlled by inference_input_type.
* NOTE: this flag only impacts real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* For real-number arrays, the impact of this flag is to allow the output
* file to choose a different real-numbers representation (quantization)
* from what the input file used. For any other types of arrays, changing
* the data type would not make sense.
* Specifically:
* - If FLOAT, then real-numbers arrays will be of type float in
* the output file. If they were quantized in the input file, then
* they get dequantized.
* - If QUANTIZED_UINT8, then real-numbers arrays will be quantized
* as uint8 in the output file. If they were float in the input file,
* then they get quantized.
* - If not set, then all real-numbers arrays retain the same type in the
* output file as they have in the input file.
*
*
* optional .toco.IODataType inference_type = 4;
*/
public toco.Types.IODataType getInferenceType() {
@SuppressWarnings("deprecation")
toco.Types.IODataType result = toco.Types.IODataType.valueOf(inferenceType_);
return result == null ? toco.Types.IODataType.IO_DATA_TYPE_UNKNOWN : result;
}
public static final int DEFAULT_RANGES_MIN_FIELD_NUMBER = 5;
private float defaultRangesMin_;
/**
*
* default_ranges_min and default_ranges_max are helpers to experiment
* with quantization of models. Normally, quantization requires the input
* model to have (min, max) range information for every activations array.
* This is needed in order to know how to quantize arrays and still achieve
* satisfactory accuracy. However, in some circumstances one would just like
* to estimate the performance of quantized inference, without caring about
* accuracy. That is what default_ranges_min and default_ranges_max are for:
* when specified, they will be used as default (min, max) range boundaries
* for all activation arrays that lack (min, max) range information, thus
* allowing for quantization to proceed.
* It should be clear from the above explanation that these parameters are
* for experimentation purposes only and should not be used in production:
* they make it easy to quantize models, but the resulting quantized model
* will be inaccurate.
* These values only apply to arrays quantized with the kUint8 data type.
*
*
* optional float default_ranges_min = 5;
*/
public boolean hasDefaultRangesMin() {
return ((bitField0_ & 0x00000010) == 0x00000010);
}
/**
*
* default_ranges_min and default_ranges_max are helpers to experiment
* with quantization of models. Normally, quantization requires the input
* model to have (min, max) range information for every activations array.
* This is needed in order to know how to quantize arrays and still achieve
* satisfactory accuracy. However, in some circumstances one would just like
* to estimate the performance of quantized inference, without caring about
* accuracy. That is what default_ranges_min and default_ranges_max are for:
* when specified, they will be used as default (min, max) range boundaries
* for all activation arrays that lack (min, max) range information, thus
* allowing for quantization to proceed.
* It should be clear from the above explanation that these parameters are
* for experimentation purposes only and should not be used in production:
* they make it easy to quantize models, but the resulting quantized model
* will be inaccurate.
* These values only apply to arrays quantized with the kUint8 data type.
*
*
* optional float default_ranges_min = 5;
*/
public float getDefaultRangesMin() {
return defaultRangesMin_;
}
public static final int DEFAULT_RANGES_MAX_FIELD_NUMBER = 6;
private float defaultRangesMax_;
/**
* optional float default_ranges_max = 6;
*/
public boolean hasDefaultRangesMax() {
return ((bitField0_ & 0x00000020) == 0x00000020);
}
/**
* optional float default_ranges_max = 6;
*/
public float getDefaultRangesMax() {
return defaultRangesMax_;
}
public static final int DEFAULT_INT16_RANGES_MIN_FIELD_NUMBER = 15;
private float defaultInt16RangesMin_;
/**
*
* Equivalent versions of default_ranges_min/_max for arrays quantized with
* the kInt16 data type.
*
*
* optional float default_int16_ranges_min = 15;
*/
public boolean hasDefaultInt16RangesMin() {
return ((bitField0_ & 0x00000040) == 0x00000040);
}
/**
*
* Equivalent versions of default_ranges_min/_max for arrays quantized with
* the kInt16 data type.
*
*
* optional float default_int16_ranges_min = 15;
*/
public float getDefaultInt16RangesMin() {
return defaultInt16RangesMin_;
}
public static final int DEFAULT_INT16_RANGES_MAX_FIELD_NUMBER = 16;
private float defaultInt16RangesMax_;
/**
* optional float default_int16_ranges_max = 16;
*/
public boolean hasDefaultInt16RangesMax() {
return ((bitField0_ & 0x00000080) == 0x00000080);
}
/**
* optional float default_int16_ranges_max = 16;
*/
public float getDefaultInt16RangesMax() {
return defaultInt16RangesMax_;
}
public static final int DROP_FAKE_QUANT_FIELD_NUMBER = 7;
private boolean dropFakeQuant_;
/**
*
* Ignore and discard FakeQuant nodes. For instance, that can be used to
* generate plain float code without fake-quantization from a quantized
* graph.
*
*
* optional bool drop_fake_quant = 7;
*/
public boolean hasDropFakeQuant() {
return ((bitField0_ & 0x00000100) == 0x00000100);
}
/**
*
* Ignore and discard FakeQuant nodes. For instance, that can be used to
* generate plain float code without fake-quantization from a quantized
* graph.
*
*
* optional bool drop_fake_quant = 7;
*/
public boolean getDropFakeQuant() {
return dropFakeQuant_;
}
public static final int REORDER_ACROSS_FAKE_QUANT_FIELD_NUMBER = 8;
private boolean reorderAcrossFakeQuant_;
/**
*
* Normally, FakeQuant nodes must be strict boundaries for graph
* transformations, in order to ensure that quantized inference has the
* exact same arithmetic behavior as quantized training --- which is the
* whole point of quantized training and of FakeQuant nodes in the first
* place. However, that entails subtle requirements on where exactly
* FakeQuant nodes must be placed in the graph. Some quantized graphs
* have FakeQuant nodes at unexpected locations, that prevent graph
* transformations that are necessary in order to generate inference
* code for these graphs. Such graphs should be fixed, but as a
* temporary work-around, setting this reorder_across_fake_quant flag
* allows toco to perform necessary graph transformations on them,
* at the cost of no longer faithfully matching inference and training
* arithmetic.
*
*
* optional bool reorder_across_fake_quant = 8;
*/
public boolean hasReorderAcrossFakeQuant() {
return ((bitField0_ & 0x00000200) == 0x00000200);
}
/**
*
* Normally, FakeQuant nodes must be strict boundaries for graph
* transformations, in order to ensure that quantized inference has the
* exact same arithmetic behavior as quantized training --- which is the
* whole point of quantized training and of FakeQuant nodes in the first
* place. However, that entails subtle requirements on where exactly
* FakeQuant nodes must be placed in the graph. Some quantized graphs
* have FakeQuant nodes at unexpected locations, that prevent graph
* transformations that are necessary in order to generate inference
* code for these graphs. Such graphs should be fixed, but as a
* temporary work-around, setting this reorder_across_fake_quant flag
* allows toco to perform necessary graph transformations on them,
* at the cost of no longer faithfully matching inference and training
* arithmetic.
*
*
* optional bool reorder_across_fake_quant = 8;
*/
public boolean getReorderAcrossFakeQuant() {
return reorderAcrossFakeQuant_;
}
public static final int ALLOW_CUSTOM_OPS_FIELD_NUMBER = 10;
private boolean allowCustomOps_;
/**
*
* If true, allow TOCO to create TF Lite Custom operators for all the
* unsupported Tensorflow ops.
*
*
* optional bool allow_custom_ops = 10;
*/
public boolean hasAllowCustomOps() {
return ((bitField0_ & 0x00000400) == 0x00000400);
}
/**
*
* If true, allow TOCO to create TF Lite Custom operators for all the
* unsupported Tensorflow ops.
*
*
* optional bool allow_custom_ops = 10;
*/
public boolean getAllowCustomOps() {
return allowCustomOps_;
}
public static final int DROP_CONTROL_DEPENDENCY_FIELD_NUMBER = 12;
private boolean dropControlDependency_;
/**
*
* Applies only to the case when the input format is TENSORFLOW_GRAPHDEF.
* If true, then control dependencies will be immediately dropped during
* import.
* If not set, the default behavior is as follows:
* - Default to false if the output format is TENSORFLOW_GRAPHDEF.
* - Default to true in all other cases.
*
*
* optional bool drop_control_dependency = 12;
*/
public boolean hasDropControlDependency() {
return ((bitField0_ & 0x00000800) == 0x00000800);
}
/**
*
* Applies only to the case when the input format is TENSORFLOW_GRAPHDEF.
* If true, then control dependencies will be immediately dropped during
* import.
* If not set, the default behavior is as follows:
* - Default to false if the output format is TENSORFLOW_GRAPHDEF.
* - Default to true in all other cases.
*
*
* optional bool drop_control_dependency = 12;
*/
public boolean getDropControlDependency() {
return dropControlDependency_;
}
public static final int DEBUG_DISABLE_RECURRENT_CELL_FUSION_FIELD_NUMBER = 13;
private boolean debugDisableRecurrentCellFusion_;
/**
*
* Disables transformations that fuse subgraphs such as known LSTMs (not all
* LSTMs are identified).
*
*
* optional bool debug_disable_recurrent_cell_fusion = 13;
*/
public boolean hasDebugDisableRecurrentCellFusion() {
return ((bitField0_ & 0x00001000) == 0x00001000);
}
/**
*
* Disables transformations that fuse subgraphs such as known LSTMs (not all
* LSTMs are identified).
*
*
* optional bool debug_disable_recurrent_cell_fusion = 13;
*/
public boolean getDebugDisableRecurrentCellFusion() {
return debugDisableRecurrentCellFusion_;
}
public static final int PROPAGATE_FAKE_QUANT_NUM_BITS_FIELD_NUMBER = 14;
private boolean propagateFakeQuantNumBits_;
/**
*
* Uses the FakeQuantWithMinMaxArgs.num_bits attribute to adjust quantized
* array data types throughout the graph. The graph must be properly annotated
* with FakeQuant* ops on at least the edges and may contain additional ops on
* the interior of the graph to widen/narrow as desired.
* Input and output array data types may change because of this propagation
* and users must be sure to query the final data_type values.
*
*
* optional bool propagate_fake_quant_num_bits = 14;
*/
public boolean hasPropagateFakeQuantNumBits() {
return ((bitField0_ & 0x00002000) == 0x00002000);
}
/**
*
* Uses the FakeQuantWithMinMaxArgs.num_bits attribute to adjust quantized
* array data types throughout the graph. The graph must be properly annotated
* with FakeQuant* ops on at least the edges and may contain additional ops on
* the interior of the graph to widen/narrow as desired.
* Input and output array data types may change because of this propagation
* and users must be sure to query the final data_type values.
*
*
* optional bool propagate_fake_quant_num_bits = 14;
*/
public boolean getPropagateFakeQuantNumBits() {
return propagateFakeQuantNumBits_;
}
public static final int ALLOW_NUDGING_WEIGHTS_TO_USE_FAST_GEMM_KERNEL_FIELD_NUMBER = 17;
private boolean allowNudgingWeightsToUseFastGemmKernel_;
/**
*
* Some fast uint8 GEMM kernels require uint8 weights to avoid the value 0.
* This flag allows nudging them to 1 to allow proceeding, with moderate
* inaccuracy.
*
*
* optional bool allow_nudging_weights_to_use_fast_gemm_kernel = 17;
*/
public boolean hasAllowNudgingWeightsToUseFastGemmKernel() {
return ((bitField0_ & 0x00004000) == 0x00004000);
}
/**
*
* Some fast uint8 GEMM kernels require uint8 weights to avoid the value 0.
* This flag allows nudging them to 1 to allow proceeding, with moderate
* inaccuracy.
*
*
* optional bool allow_nudging_weights_to_use_fast_gemm_kernel = 17;
*/
public boolean getAllowNudgingWeightsToUseFastGemmKernel() {
return allowNudgingWeightsToUseFastGemmKernel_;
}
public static final int DEDUPE_ARRAY_MIN_SIZE_BYTES_FIELD_NUMBER = 18;
private long dedupeArrayMinSizeBytes_;
/**
*
* Minimum size of constant arrays to deduplicate; arrays smaller will not be
* deduplicated.
*
*
* optional int64 dedupe_array_min_size_bytes = 18 [default = 64];
*/
public boolean hasDedupeArrayMinSizeBytes() {
return ((bitField0_ & 0x00008000) == 0x00008000);
}
/**
*
* Minimum size of constant arrays to deduplicate; arrays smaller will not be
* deduplicated.
*
*
* optional int64 dedupe_array_min_size_bytes = 18 [default = 64];
*/
public long getDedupeArrayMinSizeBytes() {
return dedupeArrayMinSizeBytes_;
}
public static final int SPLIT_TFLITE_LSTM_INPUTS_FIELD_NUMBER = 19;
private boolean splitTfliteLstmInputs_;
/**
*
* Split the LSTM inputs from 5 tensors to 18 tensors for TFLite.
* Ignored if the output format is not TFLite.
*
*
* optional bool split_tflite_lstm_inputs = 19 [default = true];
*/
public boolean hasSplitTfliteLstmInputs() {
return ((bitField0_ & 0x00010000) == 0x00010000);
}
/**
*
* Split the LSTM inputs from 5 tensors to 18 tensors for TFLite.
* Ignored if the output format is not TFLite.
*
*
* optional bool split_tflite_lstm_inputs = 19 [default = true];
*/
public boolean getSplitTfliteLstmInputs() {
return splitTfliteLstmInputs_;
}
public static final int QUANTIZE_WEIGHTS_FIELD_NUMBER = 20;
private boolean quantizeWeights_;
/**
*
* Store weights as quantized weights followed by dequantize operations.
* Computation is still done in float, but reduces model size (at the cost of
* accuracy and latency).
* DEPRECATED: Please use post_training_quantize instead.
*
*
* optional bool quantize_weights = 20 [default = false];
*/
public boolean hasQuantizeWeights() {
return ((bitField0_ & 0x00020000) == 0x00020000);
}
/**
*
* Store weights as quantized weights followed by dequantize operations.
* Computation is still done in float, but reduces model size (at the cost of
* accuracy and latency).
* DEPRECATED: Please use post_training_quantize instead.
*
*
* optional bool quantize_weights = 20 [default = false];
*/
public boolean getQuantizeWeights() {
return quantizeWeights_;
}
public static final int DUMP_GRAPHVIZ_DIR_FIELD_NUMBER = 24;
private volatile java.lang.Object dumpGraphvizDir_;
/**
*
* Full filepath of folder to dump the graphs at various stages of processing
* GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order
* to keep the requirements of the output file.
*
*
* optional string dump_graphviz_dir = 24;
*/
public boolean hasDumpGraphvizDir() {
return ((bitField0_ & 0x00040000) == 0x00040000);
}
/**
*
* Full filepath of folder to dump the graphs at various stages of processing
* GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order
* to keep the requirements of the output file.
*
*
* optional string dump_graphviz_dir = 24;
*/
public java.lang.String getDumpGraphvizDir() {
java.lang.Object ref = dumpGraphvizDir_;
if (ref instanceof java.lang.String) {
return (java.lang.String) ref;
} else {
com.google.protobuf.ByteString bs =
(com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
if (bs.isValidUtf8()) {
dumpGraphvizDir_ = s;
}
return s;
}
}
/**
*
* Full filepath of folder to dump the graphs at various stages of processing
* GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order
* to keep the requirements of the output file.
*
*
* optional string dump_graphviz_dir = 24;
*/
public com.google.protobuf.ByteString
getDumpGraphvizDirBytes() {
java.lang.Object ref = dumpGraphvizDir_;
if (ref instanceof java.lang.String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8(
(java.lang.String) ref);
dumpGraphvizDir_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
public static final int DUMP_GRAPHVIZ_INCLUDE_VIDEO_FIELD_NUMBER = 25;
private boolean dumpGraphvizIncludeVideo_;
/**
*
* Boolean indicating whether to dump the graph after every graph
* transformation.
*
*
* optional bool dump_graphviz_include_video = 25;
*/
public boolean hasDumpGraphvizIncludeVideo() {
return ((bitField0_ & 0x00080000) == 0x00080000);
}
/**
*
* Boolean indicating whether to dump the graph after every graph
* transformation.
*
*
* optional bool dump_graphviz_include_video = 25;
*/
public boolean getDumpGraphvizIncludeVideo() {
return dumpGraphvizIncludeVideo_;
}
public static final int POST_TRAINING_QUANTIZE_FIELD_NUMBER = 26;
private boolean postTrainingQuantize_;
/**
*
* Boolean indicating whether to quantize the weights of the converted float
* model. Model size will be reduced and there will be latency improvements
* (at the cost of accuracy).
*
*
* optional bool post_training_quantize = 26 [default = false];
*/
public boolean hasPostTrainingQuantize() {
return ((bitField0_ & 0x00100000) == 0x00100000);
}
/**
*
* Boolean indicating whether to quantize the weights of the converted float
* model. Model size will be reduced and there will be latency improvements
* (at the cost of accuracy).
*
*
* optional bool post_training_quantize = 26 [default = false];
*/
public boolean getPostTrainingQuantize() {
return postTrainingQuantize_;
}
public static final int ALLOW_FLEX_OPS_FIELD_NUMBER = 27;
private boolean allowFlexOps_;
/**
*
* When enabled, unsupported ops will be converted to TFLite Flex ops.
* TODO(ycling): Consider to rename the following 2 flags and don't call it
* "Flex".
* `allow_flex_ops` should always be used with `allow_custom_ops`.
* WARNING: Experimental interface, subject to change
*
*
* optional bool allow_flex_ops = 27 [default = false];
*/
public boolean hasAllowFlexOps() {
return ((bitField0_ & 0x00200000) == 0x00200000);
}
/**
*
* When enabled, unsupported ops will be converted to TFLite Flex ops.
* TODO(ycling): Consider to rename the following 2 flags and don't call it
* "Flex".
* `allow_flex_ops` should always be used with `allow_custom_ops`.
* WARNING: Experimental interface, subject to change
*
*
* optional bool allow_flex_ops = 27 [default = false];
*/
public boolean getAllowFlexOps() {
return allowFlexOps_;
}
public static final int FORCE_FLEX_OPS_FIELD_NUMBER = 28;
private boolean forceFlexOps_;
/**
*
* When enabled, all TensorFlow ops will be converted to TFLite Flex
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public com.google.protobuf.Descriptors.Descriptor
getDescriptorForType() {
return toco.TocoFlagsOuterClass.internal_static_toco_TocoFlags_descriptor;
}
@java.lang.Override
public toco.TocoFlagsOuterClass.TocoFlags getDefaultInstanceForType() {
return toco.TocoFlagsOuterClass.TocoFlags.getDefaultInstance();
}
@java.lang.Override
public toco.TocoFlagsOuterClass.TocoFlags build() {
toco.TocoFlagsOuterClass.TocoFlags result = buildPartial();
if (!result.isInitialized()) {
throw newUninitializedMessageException(result);
}
return result;
}
@java.lang.Override
public toco.TocoFlagsOuterClass.TocoFlags buildPartial() {
toco.TocoFlagsOuterClass.TocoFlags result = new toco.TocoFlagsOuterClass.TocoFlags(this);
int from_bitField0_ = bitField0_;
int to_bitField0_ = 0;
if (((from_bitField0_ & 0x00000001) == 0x00000001)) {
to_bitField0_ |= 0x00000001;
}
result.inputFormat_ = inputFormat_;
if (((from_bitField0_ & 0x00000002) == 0x00000002)) {
to_bitField0_ |= 0x00000002;
}
result.outputFormat_ = outputFormat_;
if (((from_bitField0_ & 0x00000004) == 0x00000004)) {
to_bitField0_ |= 0x00000004;
}
result.inferenceInputType_ = inferenceInputType_;
if (((from_bitField0_ & 0x00000008) == 0x00000008)) {
to_bitField0_ |= 0x00000008;
}
result.inferenceType_ = inferenceType_;
if (((from_bitField0_ & 0x00000010) == 0x00000010)) {
to_bitField0_ |= 0x00000010;
}
result.defaultRangesMin_ = defaultRangesMin_;
if (((from_bitField0_ & 0x00000020) == 0x00000020)) {
to_bitField0_ |= 0x00000020;
}
result.defaultRangesMax_ = defaultRangesMax_;
if (((from_bitField0_ & 0x00000040) == 0x00000040)) {
to_bitField0_ |= 0x00000040;
}
result.defaultInt16RangesMin_ = defaultInt16RangesMin_;
if (((from_bitField0_ & 0x00000080) == 0x00000080)) {
to_bitField0_ |= 0x00000080;
}
result.defaultInt16RangesMax_ = defaultInt16RangesMax_;
if (((from_bitField0_ & 0x00000100) == 0x00000100)) {
to_bitField0_ |= 0x00000100;
}
result.dropFakeQuant_ = dropFakeQuant_;
if (((from_bitField0_ & 0x00000200) == 0x00000200)) {
to_bitField0_ |= 0x00000200;
}
result.reorderAcrossFakeQuant_ = reorderAcrossFakeQuant_;
if (((from_bitField0_ & 0x00000400) == 0x00000400)) {
to_bitField0_ |= 0x00000400;
}
result.allowCustomOps_ = allowCustomOps_;
if (((from_bitField0_ & 0x00000800) == 0x00000800)) {
to_bitField0_ |= 0x00000800;
}
result.dropControlDependency_ = dropControlDependency_;
if (((from_bitField0_ & 0x00001000) == 0x00001000)) {
to_bitField0_ |= 0x00001000;
}
result.debugDisableRecurrentCellFusion_ = debugDisableRecurrentCellFusion_;
if (((from_bitField0_ & 0x00002000) == 0x00002000)) {
to_bitField0_ |= 0x00002000;
}
result.propagateFakeQuantNumBits_ = propagateFakeQuantNumBits_;
if (((from_bitField0_ & 0x00004000) == 0x00004000)) {
to_bitField0_ |= 0x00004000;
}
result.allowNudgingWeightsToUseFastGemmKernel_ = allowNudgingWeightsToUseFastGemmKernel_;
if (((from_bitField0_ & 0x00008000) == 0x00008000)) {
to_bitField0_ |= 0x00008000;
}
result.dedupeArrayMinSizeBytes_ = dedupeArrayMinSizeBytes_;
if (((from_bitField0_ & 0x00010000) == 0x00010000)) {
to_bitField0_ |= 0x00010000;
}
result.splitTfliteLstmInputs_ = splitTfliteLstmInputs_;
if (((from_bitField0_ & 0x00020000) == 0x00020000)) {
to_bitField0_ |= 0x00020000;
}
result.quantizeWeights_ = quantizeWeights_;
if (((from_bitField0_ & 0x00040000) == 0x00040000)) {
to_bitField0_ |= 0x00040000;
}
result.dumpGraphvizDir_ = dumpGraphvizDir_;
if (((from_bitField0_ & 0x00080000) == 0x00080000)) {
to_bitField0_ |= 0x00080000;
}
result.dumpGraphvizIncludeVideo_ = dumpGraphvizIncludeVideo_;
if (((from_bitField0_ & 0x00100000) == 0x00100000)) {
to_bitField0_ |= 0x00100000;
}
result.postTrainingQuantize_ = postTrainingQuantize_;
if (((from_bitField0_ & 0x00200000) == 0x00200000)) {
to_bitField0_ |= 0x00200000;
}
result.allowFlexOps_ = allowFlexOps_;
if (((from_bitField0_ & 0x00400000) == 0x00400000)) {
to_bitField0_ |= 0x00400000;
}
result.forceFlexOps_ = forceFlexOps_;
result.bitField0_ = to_bitField0_;
onBuilt();
return result;
}
@java.lang.Override
public Builder clone() {
return (Builder) super.clone();
}
@java.lang.Override
public Builder setField(
com.google.protobuf.Descriptors.FieldDescriptor field,
java.lang.Object value) {
return (Builder) super.setField(field, value);
}
@java.lang.Override
public Builder clearField(
com.google.protobuf.Descriptors.FieldDescriptor field) {
return (Builder) super.clearField(field);
}
@java.lang.Override
public Builder clearOneof(
com.google.protobuf.Descriptors.OneofDescriptor oneof) {
return (Builder) super.clearOneof(oneof);
}
@java.lang.Override
public Builder setRepeatedField(
com.google.protobuf.Descriptors.FieldDescriptor field,
int index, java.lang.Object value) {
return (Builder) super.setRepeatedField(field, index, value);
}
@java.lang.Override
public Builder addRepeatedField(
com.google.protobuf.Descriptors.FieldDescriptor field,
java.lang.Object value) {
return (Builder) super.addRepeatedField(field, value);
}
@java.lang.Override
public Builder mergeFrom(com.google.protobuf.Message other) {
if (other instanceof toco.TocoFlagsOuterClass.TocoFlags) {
return mergeFrom((toco.TocoFlagsOuterClass.TocoFlags)other);
} else {
super.mergeFrom(other);
return this;
}
}
public Builder mergeFrom(toco.TocoFlagsOuterClass.TocoFlags other) {
if (other == toco.TocoFlagsOuterClass.TocoFlags.getDefaultInstance()) return this;
if (other.hasInputFormat()) {
setInputFormat(other.getInputFormat());
}
if (other.hasOutputFormat()) {
setOutputFormat(other.getOutputFormat());
}
if (other.hasInferenceInputType()) {
setInferenceInputType(other.getInferenceInputType());
}
if (other.hasInferenceType()) {
setInferenceType(other.getInferenceType());
}
if (other.hasDefaultRangesMin()) {
setDefaultRangesMin(other.getDefaultRangesMin());
}
if (other.hasDefaultRangesMax()) {
setDefaultRangesMax(other.getDefaultRangesMax());
}
if (other.hasDefaultInt16RangesMin()) {
setDefaultInt16RangesMin(other.getDefaultInt16RangesMin());
}
if (other.hasDefaultInt16RangesMax()) {
setDefaultInt16RangesMax(other.getDefaultInt16RangesMax());
}
if (other.hasDropFakeQuant()) {
setDropFakeQuant(other.getDropFakeQuant());
}
if (other.hasReorderAcrossFakeQuant()) {
setReorderAcrossFakeQuant(other.getReorderAcrossFakeQuant());
}
if (other.hasAllowCustomOps()) {
setAllowCustomOps(other.getAllowCustomOps());
}
if (other.hasDropControlDependency()) {
setDropControlDependency(other.getDropControlDependency());
}
if (other.hasDebugDisableRecurrentCellFusion()) {
setDebugDisableRecurrentCellFusion(other.getDebugDisableRecurrentCellFusion());
}
if (other.hasPropagateFakeQuantNumBits()) {
setPropagateFakeQuantNumBits(other.getPropagateFakeQuantNumBits());
}
if (other.hasAllowNudgingWeightsToUseFastGemmKernel()) {
setAllowNudgingWeightsToUseFastGemmKernel(other.getAllowNudgingWeightsToUseFastGemmKernel());
}
if (other.hasDedupeArrayMinSizeBytes()) {
setDedupeArrayMinSizeBytes(other.getDedupeArrayMinSizeBytes());
}
if (other.hasSplitTfliteLstmInputs()) {
setSplitTfliteLstmInputs(other.getSplitTfliteLstmInputs());
}
if (other.hasQuantizeWeights()) {
setQuantizeWeights(other.getQuantizeWeights());
}
if (other.hasDumpGraphvizDir()) {
bitField0_ |= 0x00040000;
dumpGraphvizDir_ = other.dumpGraphvizDir_;
onChanged();
}
if (other.hasDumpGraphvizIncludeVideo()) {
setDumpGraphvizIncludeVideo(other.getDumpGraphvizIncludeVideo());
}
if (other.hasPostTrainingQuantize()) {
setPostTrainingQuantize(other.getPostTrainingQuantize());
}
if (other.hasAllowFlexOps()) {
setAllowFlexOps(other.getAllowFlexOps());
}
if (other.hasForceFlexOps()) {
setForceFlexOps(other.getForceFlexOps());
}
this.mergeUnknownFields(other.unknownFields);
onChanged();
return this;
}
@java.lang.Override
public final boolean isInitialized() {
return true;
}
@java.lang.Override
public Builder mergeFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
toco.TocoFlagsOuterClass.TocoFlags parsedMessage = null;
try {
parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry);
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
parsedMessage = (toco.TocoFlagsOuterClass.TocoFlags) e.getUnfinishedMessage();
throw e.unwrapIOException();
} finally {
if (parsedMessage != null) {
mergeFrom(parsedMessage);
}
}
return this;
}
private int bitField0_;
private int inputFormat_ = 0;
/**
*
* Input file format
*
*
* optional .toco.FileFormat input_format = 1;
*/
public boolean hasInputFormat() {
return ((bitField0_ & 0x00000001) == 0x00000001);
}
/**
*
* Input file format
*
*
* optional .toco.FileFormat input_format = 1;
*/
public toco.TocoFlagsOuterClass.FileFormat getInputFormat() {
@SuppressWarnings("deprecation")
toco.TocoFlagsOuterClass.FileFormat result = toco.TocoFlagsOuterClass.FileFormat.valueOf(inputFormat_);
return result == null ? toco.TocoFlagsOuterClass.FileFormat.FILE_FORMAT_UNKNOWN : result;
}
/**
*
* Input file format
*
*
* optional .toco.FileFormat input_format = 1;
*/
public Builder setInputFormat(toco.TocoFlagsOuterClass.FileFormat value) {
if (value == null) {
throw new NullPointerException();
}
bitField0_ |= 0x00000001;
inputFormat_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Input file format
*
*
* optional .toco.FileFormat input_format = 1;
*/
public Builder clearInputFormat() {
bitField0_ = (bitField0_ & ~0x00000001);
inputFormat_ = 0;
onChanged();
return this;
}
private int outputFormat_ = 0;
/**
*
* Output file format
*
*
* optional .toco.FileFormat output_format = 2;
*/
public boolean hasOutputFormat() {
return ((bitField0_ & 0x00000002) == 0x00000002);
}
/**
*
* Output file format
*
*
* optional .toco.FileFormat output_format = 2;
*/
public toco.TocoFlagsOuterClass.FileFormat getOutputFormat() {
@SuppressWarnings("deprecation")
toco.TocoFlagsOuterClass.FileFormat result = toco.TocoFlagsOuterClass.FileFormat.valueOf(outputFormat_);
return result == null ? toco.TocoFlagsOuterClass.FileFormat.FILE_FORMAT_UNKNOWN : result;
}
/**
*
* Output file format
*
*
* optional .toco.FileFormat output_format = 2;
*/
public Builder setOutputFormat(toco.TocoFlagsOuterClass.FileFormat value) {
if (value == null) {
throw new NullPointerException();
}
bitField0_ |= 0x00000002;
outputFormat_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Output file format
*
*
* optional .toco.FileFormat output_format = 2;
*/
public Builder clearOutputFormat() {
bitField0_ = (bitField0_ & ~0x00000002);
outputFormat_ = 0;
onChanged();
return this;
}
private int inferenceInputType_ = 0;
/**
*
* Similar to inference_type, but allows to control specifically the
* quantization of input arrays, separately from other arrays.
* If not set, then the value of inference_type is implicitly used, i.e.
* by default input arrays are quantized like other arrays.
* Like inference_type, this only affects real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* The typical use for this flag is for vision models taking a bitmap
* as input, typically with uint8 channels, yet still requiring floating-point
* inference. For such image models, the uint8 input is quantized, i.e.
* the uint8 values are interpreted as real numbers, and the quantization
* parameters used for such input arrays are their mean_value, std_value
* parameters.
*
*
* optional .toco.IODataType inference_input_type = 11;
*/
public boolean hasInferenceInputType() {
return ((bitField0_ & 0x00000004) == 0x00000004);
}
/**
*
* Similar to inference_type, but allows to control specifically the
* quantization of input arrays, separately from other arrays.
* If not set, then the value of inference_type is implicitly used, i.e.
* by default input arrays are quantized like other arrays.
* Like inference_type, this only affects real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* The typical use for this flag is for vision models taking a bitmap
* as input, typically with uint8 channels, yet still requiring floating-point
* inference. For such image models, the uint8 input is quantized, i.e.
* the uint8 values are interpreted as real numbers, and the quantization
* parameters used for such input arrays are their mean_value, std_value
* parameters.
*
*
* optional .toco.IODataType inference_input_type = 11;
*/
public toco.Types.IODataType getInferenceInputType() {
@SuppressWarnings("deprecation")
toco.Types.IODataType result = toco.Types.IODataType.valueOf(inferenceInputType_);
return result == null ? toco.Types.IODataType.IO_DATA_TYPE_UNKNOWN : result;
}
/**
*
* Similar to inference_type, but allows to control specifically the
* quantization of input arrays, separately from other arrays.
* If not set, then the value of inference_type is implicitly used, i.e.
* by default input arrays are quantized like other arrays.
* Like inference_type, this only affects real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* The typical use for this flag is for vision models taking a bitmap
* as input, typically with uint8 channels, yet still requiring floating-point
* inference. For such image models, the uint8 input is quantized, i.e.
* the uint8 values are interpreted as real numbers, and the quantization
* parameters used for such input arrays are their mean_value, std_value
* parameters.
*
*
* optional .toco.IODataType inference_input_type = 11;
*/
public Builder setInferenceInputType(toco.Types.IODataType value) {
if (value == null) {
throw new NullPointerException();
}
bitField0_ |= 0x00000004;
inferenceInputType_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Similar to inference_type, but allows to control specifically the
* quantization of input arrays, separately from other arrays.
* If not set, then the value of inference_type is implicitly used, i.e.
* by default input arrays are quantized like other arrays.
* Like inference_type, this only affects real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* The typical use for this flag is for vision models taking a bitmap
* as input, typically with uint8 channels, yet still requiring floating-point
* inference. For such image models, the uint8 input is quantized, i.e.
* the uint8 values are interpreted as real numbers, and the quantization
* parameters used for such input arrays are their mean_value, std_value
* parameters.
*
*
* optional .toco.IODataType inference_input_type = 11;
*/
public Builder clearInferenceInputType() {
bitField0_ = (bitField0_ & ~0x00000004);
inferenceInputType_ = 0;
onChanged();
return this;
}
private int inferenceType_ = 0;
/**
*
* Sets the type of real-number arrays in the output file, that is, controls
* the representation (quantization) of real numbers in the output file,
* except for input arrays, which are controlled by inference_input_type.
* NOTE: this flag only impacts real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* For real-number arrays, the impact of this flag is to allow the output
* file to choose a different real-numbers representation (quantization)
* from what the input file used. For any other types of arrays, changing
* the data type would not make sense.
* Specifically:
* - If FLOAT, then real-numbers arrays will be of type float in
* the output file. If they were quantized in the input file, then
* they get dequantized.
* - If QUANTIZED_UINT8, then real-numbers arrays will be quantized
* as uint8 in the output file. If they were float in the input file,
* then they get quantized.
* - If not set, then all real-numbers arrays retain the same type in the
* output file as they have in the input file.
*
*
* optional .toco.IODataType inference_type = 4;
*/
public boolean hasInferenceType() {
return ((bitField0_ & 0x00000008) == 0x00000008);
}
/**
*
* Sets the type of real-number arrays in the output file, that is, controls
* the representation (quantization) of real numbers in the output file,
* except for input arrays, which are controlled by inference_input_type.
* NOTE: this flag only impacts real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* For real-number arrays, the impact of this flag is to allow the output
* file to choose a different real-numbers representation (quantization)
* from what the input file used. For any other types of arrays, changing
* the data type would not make sense.
* Specifically:
* - If FLOAT, then real-numbers arrays will be of type float in
* the output file. If they were quantized in the input file, then
* they get dequantized.
* - If QUANTIZED_UINT8, then real-numbers arrays will be quantized
* as uint8 in the output file. If they were float in the input file,
* then they get quantized.
* - If not set, then all real-numbers arrays retain the same type in the
* output file as they have in the input file.
*
*
* optional .toco.IODataType inference_type = 4;
*/
public toco.Types.IODataType getInferenceType() {
@SuppressWarnings("deprecation")
toco.Types.IODataType result = toco.Types.IODataType.valueOf(inferenceType_);
return result == null ? toco.Types.IODataType.IO_DATA_TYPE_UNKNOWN : result;
}
/**
*
* Sets the type of real-number arrays in the output file, that is, controls
* the representation (quantization) of real numbers in the output file,
* except for input arrays, which are controlled by inference_input_type.
* NOTE: this flag only impacts real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* For real-number arrays, the impact of this flag is to allow the output
* file to choose a different real-numbers representation (quantization)
* from what the input file used. For any other types of arrays, changing
* the data type would not make sense.
* Specifically:
* - If FLOAT, then real-numbers arrays will be of type float in
* the output file. If they were quantized in the input file, then
* they get dequantized.
* - If QUANTIZED_UINT8, then real-numbers arrays will be quantized
* as uint8 in the output file. If they were float in the input file,
* then they get quantized.
* - If not set, then all real-numbers arrays retain the same type in the
* output file as they have in the input file.
*
*
* optional .toco.IODataType inference_type = 4;
*/
public Builder setInferenceType(toco.Types.IODataType value) {
if (value == null) {
throw new NullPointerException();
}
bitField0_ |= 0x00000008;
inferenceType_ = value.getNumber();
onChanged();
return this;
}
/**
*
* Sets the type of real-number arrays in the output file, that is, controls
* the representation (quantization) of real numbers in the output file,
* except for input arrays, which are controlled by inference_input_type.
* NOTE: this flag only impacts real-number arrays. By "real-number"
* we mean float arrays, and quantized arrays. This excludes plain
* integer arrays, strings arrays, and every other data type.
* For real-number arrays, the impact of this flag is to allow the output
* file to choose a different real-numbers representation (quantization)
* from what the input file used. For any other types of arrays, changing
* the data type would not make sense.
* Specifically:
* - If FLOAT, then real-numbers arrays will be of type float in
* the output file. If they were quantized in the input file, then
* they get dequantized.
* - If QUANTIZED_UINT8, then real-numbers arrays will be quantized
* as uint8 in the output file. If they were float in the input file,
* then they get quantized.
* - If not set, then all real-numbers arrays retain the same type in the
* output file as they have in the input file.
*
*
* optional .toco.IODataType inference_type = 4;
*/
public Builder clearInferenceType() {
bitField0_ = (bitField0_ & ~0x00000008);
inferenceType_ = 0;
onChanged();
return this;
}
private float defaultRangesMin_ ;
/**
*
* default_ranges_min and default_ranges_max are helpers to experiment
* with quantization of models. Normally, quantization requires the input
* model to have (min, max) range information for every activations array.
* This is needed in order to know how to quantize arrays and still achieve
* satisfactory accuracy. However, in some circumstances one would just like
* to estimate the performance of quantized inference, without caring about
* accuracy. That is what default_ranges_min and default_ranges_max are for:
* when specified, they will be used as default (min, max) range boundaries
* for all activation arrays that lack (min, max) range information, thus
* allowing for quantization to proceed.
* It should be clear from the above explanation that these parameters are
* for experimentation purposes only and should not be used in production:
* they make it easy to quantize models, but the resulting quantized model
* will be inaccurate.
* These values only apply to arrays quantized with the kUint8 data type.
*
*
* optional float default_ranges_min = 5;
*/
public boolean hasDefaultRangesMin() {
return ((bitField0_ & 0x00000010) == 0x00000010);
}
/**
*
* default_ranges_min and default_ranges_max are helpers to experiment
* with quantization of models. Normally, quantization requires the input
* model to have (min, max) range information for every activations array.
* This is needed in order to know how to quantize arrays and still achieve
* satisfactory accuracy. However, in some circumstances one would just like
* to estimate the performance of quantized inference, without caring about
* accuracy. That is what default_ranges_min and default_ranges_max are for:
* when specified, they will be used as default (min, max) range boundaries
* for all activation arrays that lack (min, max) range information, thus
* allowing for quantization to proceed.
* It should be clear from the above explanation that these parameters are
* for experimentation purposes only and should not be used in production:
* they make it easy to quantize models, but the resulting quantized model
* will be inaccurate.
* These values only apply to arrays quantized with the kUint8 data type.
*
*
* optional float default_ranges_min = 5;
*/
public float getDefaultRangesMin() {
return defaultRangesMin_;
}
/**
*
* default_ranges_min and default_ranges_max are helpers to experiment
* with quantization of models. Normally, quantization requires the input
* model to have (min, max) range information for every activations array.
* This is needed in order to know how to quantize arrays and still achieve
* satisfactory accuracy. However, in some circumstances one would just like
* to estimate the performance of quantized inference, without caring about
* accuracy. That is what default_ranges_min and default_ranges_max are for:
* when specified, they will be used as default (min, max) range boundaries
* for all activation arrays that lack (min, max) range information, thus
* allowing for quantization to proceed.
* It should be clear from the above explanation that these parameters are
* for experimentation purposes only and should not be used in production:
* they make it easy to quantize models, but the resulting quantized model
* will be inaccurate.
* These values only apply to arrays quantized with the kUint8 data type.
*
*
* optional float default_ranges_min = 5;
*/
public Builder setDefaultRangesMin(float value) {
bitField0_ |= 0x00000010;
defaultRangesMin_ = value;
onChanged();
return this;
}
/**
*
* default_ranges_min and default_ranges_max are helpers to experiment
* with quantization of models. Normally, quantization requires the input
* model to have (min, max) range information for every activations array.
* This is needed in order to know how to quantize arrays and still achieve
* satisfactory accuracy. However, in some circumstances one would just like
* to estimate the performance of quantized inference, without caring about
* accuracy. That is what default_ranges_min and default_ranges_max are for:
* when specified, they will be used as default (min, max) range boundaries
* for all activation arrays that lack (min, max) range information, thus
* allowing for quantization to proceed.
* It should be clear from the above explanation that these parameters are
* for experimentation purposes only and should not be used in production:
* they make it easy to quantize models, but the resulting quantized model
* will be inaccurate.
* These values only apply to arrays quantized with the kUint8 data type.
*
*
* optional float default_ranges_min = 5;
*/
public Builder clearDefaultRangesMin() {
bitField0_ = (bitField0_ & ~0x00000010);
defaultRangesMin_ = 0F;
onChanged();
return this;
}
private float defaultRangesMax_ ;
/**
* optional float default_ranges_max = 6;
*/
public boolean hasDefaultRangesMax() {
return ((bitField0_ & 0x00000020) == 0x00000020);
}
/**
* optional float default_ranges_max = 6;
*/
public float getDefaultRangesMax() {
return defaultRangesMax_;
}
/**
* optional float default_ranges_max = 6;
*/
public Builder setDefaultRangesMax(float value) {
bitField0_ |= 0x00000020;
defaultRangesMax_ = value;
onChanged();
return this;
}
/**
* optional float default_ranges_max = 6;
*/
public Builder clearDefaultRangesMax() {
bitField0_ = (bitField0_ & ~0x00000020);
defaultRangesMax_ = 0F;
onChanged();
return this;
}
private float defaultInt16RangesMin_ ;
/**
*
* Equivalent versions of default_ranges_min/_max for arrays quantized with
* the kInt16 data type.
*
*
* optional float default_int16_ranges_min = 15;
*/
public boolean hasDefaultInt16RangesMin() {
return ((bitField0_ & 0x00000040) == 0x00000040);
}
/**
*
* Equivalent versions of default_ranges_min/_max for arrays quantized with
* the kInt16 data type.
*
*
* optional float default_int16_ranges_min = 15;
*/
public float getDefaultInt16RangesMin() {
return defaultInt16RangesMin_;
}
/**
*
* Equivalent versions of default_ranges_min/_max for arrays quantized with
* the kInt16 data type.
*
*
* optional float default_int16_ranges_min = 15;
*/
public Builder setDefaultInt16RangesMin(float value) {
bitField0_ |= 0x00000040;
defaultInt16RangesMin_ = value;
onChanged();
return this;
}
/**
*
* Equivalent versions of default_ranges_min/_max for arrays quantized with
* the kInt16 data type.
*
*
* optional float default_int16_ranges_min = 15;
*/
public Builder clearDefaultInt16RangesMin() {
bitField0_ = (bitField0_ & ~0x00000040);
defaultInt16RangesMin_ = 0F;
onChanged();
return this;
}
private float defaultInt16RangesMax_ ;
/**
* optional float default_int16_ranges_max = 16;
*/
public boolean hasDefaultInt16RangesMax() {
return ((bitField0_ & 0x00000080) == 0x00000080);
}
/**
* optional float default_int16_ranges_max = 16;
*/
public float getDefaultInt16RangesMax() {
return defaultInt16RangesMax_;
}
/**
* optional float default_int16_ranges_max = 16;
*/
public Builder setDefaultInt16RangesMax(float value) {
bitField0_ |= 0x00000080;
defaultInt16RangesMax_ = value;
onChanged();
return this;
}
/**
* optional float default_int16_ranges_max = 16;
*/
public Builder clearDefaultInt16RangesMax() {
bitField0_ = (bitField0_ & ~0x00000080);
defaultInt16RangesMax_ = 0F;
onChanged();
return this;
}
private boolean dropFakeQuant_ ;
/**
*
* Ignore and discard FakeQuant nodes. For instance, that can be used to
* generate plain float code without fake-quantization from a quantized
* graph.
*
*
* optional bool drop_fake_quant = 7;
*/
public boolean hasDropFakeQuant() {
return ((bitField0_ & 0x00000100) == 0x00000100);
}
/**
*
* Ignore and discard FakeQuant nodes. For instance, that can be used to
* generate plain float code without fake-quantization from a quantized
* graph.
*
*
* optional bool drop_fake_quant = 7;
*/
public boolean getDropFakeQuant() {
return dropFakeQuant_;
}
/**
*
* Ignore and discard FakeQuant nodes. For instance, that can be used to
* generate plain float code without fake-quantization from a quantized
* graph.
*
*
* optional bool drop_fake_quant = 7;
*/
public Builder setDropFakeQuant(boolean value) {
bitField0_ |= 0x00000100;
dropFakeQuant_ = value;
onChanged();
return this;
}
/**
*
* Ignore and discard FakeQuant nodes. For instance, that can be used to
* generate plain float code without fake-quantization from a quantized
* graph.
*
*
* optional bool drop_fake_quant = 7;
*/
public Builder clearDropFakeQuant() {
bitField0_ = (bitField0_ & ~0x00000100);
dropFakeQuant_ = false;
onChanged();
return this;
}
private boolean reorderAcrossFakeQuant_ ;
/**
*
* Normally, FakeQuant nodes must be strict boundaries for graph
* transformations, in order to ensure that quantized inference has the
* exact same arithmetic behavior as quantized training --- which is the
* whole point of quantized training and of FakeQuant nodes in the first
* place. However, that entails subtle requirements on where exactly
* FakeQuant nodes must be placed in the graph. Some quantized graphs
* have FakeQuant nodes at unexpected locations, that prevent graph
* transformations that are necessary in order to generate inference
* code for these graphs. Such graphs should be fixed, but as a
* temporary work-around, setting this reorder_across_fake_quant flag
* allows toco to perform necessary graph transformations on them,
* at the cost of no longer faithfully matching inference and training
* arithmetic.
*
*
* optional bool reorder_across_fake_quant = 8;
*/
public boolean hasReorderAcrossFakeQuant() {
return ((bitField0_ & 0x00000200) == 0x00000200);
}
/**
*
* Normally, FakeQuant nodes must be strict boundaries for graph
* transformations, in order to ensure that quantized inference has the
* exact same arithmetic behavior as quantized training --- which is the
* whole point of quantized training and of FakeQuant nodes in the first
* place. However, that entails subtle requirements on where exactly
* FakeQuant nodes must be placed in the graph. Some quantized graphs
* have FakeQuant nodes at unexpected locations, that prevent graph
* transformations that are necessary in order to generate inference
* code for these graphs. Such graphs should be fixed, but as a
* temporary work-around, setting this reorder_across_fake_quant flag
* allows toco to perform necessary graph transformations on them,
* at the cost of no longer faithfully matching inference and training
* arithmetic.
*
*
* optional bool reorder_across_fake_quant = 8;
*/
public boolean getReorderAcrossFakeQuant() {
return reorderAcrossFakeQuant_;
}
/**
*
* Normally, FakeQuant nodes must be strict boundaries for graph
* transformations, in order to ensure that quantized inference has the
* exact same arithmetic behavior as quantized training --- which is the
* whole point of quantized training and of FakeQuant nodes in the first
* place. However, that entails subtle requirements on where exactly
* FakeQuant nodes must be placed in the graph. Some quantized graphs
* have FakeQuant nodes at unexpected locations, that prevent graph
* transformations that are necessary in order to generate inference
* code for these graphs. Such graphs should be fixed, but as a
* temporary work-around, setting this reorder_across_fake_quant flag
* allows toco to perform necessary graph transformations on them,
* at the cost of no longer faithfully matching inference and training
* arithmetic.
*
*
* optional bool reorder_across_fake_quant = 8;
*/
public Builder setReorderAcrossFakeQuant(boolean value) {
bitField0_ |= 0x00000200;
reorderAcrossFakeQuant_ = value;
onChanged();
return this;
}
/**
*
* Normally, FakeQuant nodes must be strict boundaries for graph
* transformations, in order to ensure that quantized inference has the
* exact same arithmetic behavior as quantized training --- which is the
* whole point of quantized training and of FakeQuant nodes in the first
* place. However, that entails subtle requirements on where exactly
* FakeQuant nodes must be placed in the graph. Some quantized graphs
* have FakeQuant nodes at unexpected locations, that prevent graph
* transformations that are necessary in order to generate inference
* code for these graphs. Such graphs should be fixed, but as a
* temporary work-around, setting this reorder_across_fake_quant flag
* allows toco to perform necessary graph transformations on them,
* at the cost of no longer faithfully matching inference and training
* arithmetic.
*
*
* optional bool reorder_across_fake_quant = 8;
*/
public Builder clearReorderAcrossFakeQuant() {
bitField0_ = (bitField0_ & ~0x00000200);
reorderAcrossFakeQuant_ = false;
onChanged();
return this;
}
private boolean allowCustomOps_ ;
/**
*
* If true, allow TOCO to create TF Lite Custom operators for all the
* unsupported Tensorflow ops.
*
*
* optional bool allow_custom_ops = 10;
*/
public boolean hasAllowCustomOps() {
return ((bitField0_ & 0x00000400) == 0x00000400);
}
/**
*
* If true, allow TOCO to create TF Lite Custom operators for all the
* unsupported Tensorflow ops.
*
*
* optional bool allow_custom_ops = 10;
*/
public boolean getAllowCustomOps() {
return allowCustomOps_;
}
/**
*
* If true, allow TOCO to create TF Lite Custom operators for all the
* unsupported Tensorflow ops.
*
*
* optional bool allow_custom_ops = 10;
*/
public Builder setAllowCustomOps(boolean value) {
bitField0_ |= 0x00000400;
allowCustomOps_ = value;
onChanged();
return this;
}
/**
*
* If true, allow TOCO to create TF Lite Custom operators for all the
* unsupported Tensorflow ops.
*
*
* optional bool allow_custom_ops = 10;
*/
public Builder clearAllowCustomOps() {
bitField0_ = (bitField0_ & ~0x00000400);
allowCustomOps_ = false;
onChanged();
return this;
}
private boolean dropControlDependency_ ;
/**
*
* Applies only to the case when the input format is TENSORFLOW_GRAPHDEF.
* If true, then control dependencies will be immediately dropped during
* import.
* If not set, the default behavior is as follows:
* - Default to false if the output format is TENSORFLOW_GRAPHDEF.
* - Default to true in all other cases.
*
*
* optional bool drop_control_dependency = 12;
*/
public boolean hasDropControlDependency() {
return ((bitField0_ & 0x00000800) == 0x00000800);
}
/**
*
* Applies only to the case when the input format is TENSORFLOW_GRAPHDEF.
* If true, then control dependencies will be immediately dropped during
* import.
* If not set, the default behavior is as follows:
* - Default to false if the output format is TENSORFLOW_GRAPHDEF.
* - Default to true in all other cases.
*
*
* optional bool drop_control_dependency = 12;
*/
public boolean getDropControlDependency() {
return dropControlDependency_;
}
/**
*
* Applies only to the case when the input format is TENSORFLOW_GRAPHDEF.
* If true, then control dependencies will be immediately dropped during
* import.
* If not set, the default behavior is as follows:
* - Default to false if the output format is TENSORFLOW_GRAPHDEF.
* - Default to true in all other cases.
*
*
* optional bool drop_control_dependency = 12;
*/
public Builder setDropControlDependency(boolean value) {
bitField0_ |= 0x00000800;
dropControlDependency_ = value;
onChanged();
return this;
}
/**
*
* Applies only to the case when the input format is TENSORFLOW_GRAPHDEF.
* If true, then control dependencies will be immediately dropped during
* import.
* If not set, the default behavior is as follows:
* - Default to false if the output format is TENSORFLOW_GRAPHDEF.
* - Default to true in all other cases.
*
*
* optional bool drop_control_dependency = 12;
*/
public Builder clearDropControlDependency() {
bitField0_ = (bitField0_ & ~0x00000800);
dropControlDependency_ = false;
onChanged();
return this;
}
private boolean debugDisableRecurrentCellFusion_ ;
/**
*
* Disables transformations that fuse subgraphs such as known LSTMs (not all
* LSTMs are identified).
*
*
* optional bool debug_disable_recurrent_cell_fusion = 13;
*/
public boolean hasDebugDisableRecurrentCellFusion() {
return ((bitField0_ & 0x00001000) == 0x00001000);
}
/**
*
* Disables transformations that fuse subgraphs such as known LSTMs (not all
* LSTMs are identified).
*
*
* optional bool debug_disable_recurrent_cell_fusion = 13;
*/
public boolean getDebugDisableRecurrentCellFusion() {
return debugDisableRecurrentCellFusion_;
}
/**
*
* Disables transformations that fuse subgraphs such as known LSTMs (not all
* LSTMs are identified).
*
*
* optional bool debug_disable_recurrent_cell_fusion = 13;
*/
public Builder setDebugDisableRecurrentCellFusion(boolean value) {
bitField0_ |= 0x00001000;
debugDisableRecurrentCellFusion_ = value;
onChanged();
return this;
}
/**
*
* Disables transformations that fuse subgraphs such as known LSTMs (not all
* LSTMs are identified).
*
*
* optional bool debug_disable_recurrent_cell_fusion = 13;
*/
public Builder clearDebugDisableRecurrentCellFusion() {
bitField0_ = (bitField0_ & ~0x00001000);
debugDisableRecurrentCellFusion_ = false;
onChanged();
return this;
}
private boolean propagateFakeQuantNumBits_ ;
/**
*
* Uses the FakeQuantWithMinMaxArgs.num_bits attribute to adjust quantized
* array data types throughout the graph. The graph must be properly annotated
* with FakeQuant* ops on at least the edges and may contain additional ops on
* the interior of the graph to widen/narrow as desired.
* Input and output array data types may change because of this propagation
* and users must be sure to query the final data_type values.
*
*
* optional bool propagate_fake_quant_num_bits = 14;
*/
public boolean hasPropagateFakeQuantNumBits() {
return ((bitField0_ & 0x00002000) == 0x00002000);
}
/**
*
* Uses the FakeQuantWithMinMaxArgs.num_bits attribute to adjust quantized
* array data types throughout the graph. The graph must be properly annotated
* with FakeQuant* ops on at least the edges and may contain additional ops on
* the interior of the graph to widen/narrow as desired.
* Input and output array data types may change because of this propagation
* and users must be sure to query the final data_type values.
*
*
* optional bool propagate_fake_quant_num_bits = 14;
*/
public boolean getPropagateFakeQuantNumBits() {
return propagateFakeQuantNumBits_;
}
/**
*
* Uses the FakeQuantWithMinMaxArgs.num_bits attribute to adjust quantized
* array data types throughout the graph. The graph must be properly annotated
* with FakeQuant* ops on at least the edges and may contain additional ops on
* the interior of the graph to widen/narrow as desired.
* Input and output array data types may change because of this propagation
* and users must be sure to query the final data_type values.
*
*
* optional bool propagate_fake_quant_num_bits = 14;
*/
public Builder setPropagateFakeQuantNumBits(boolean value) {
bitField0_ |= 0x00002000;
propagateFakeQuantNumBits_ = value;
onChanged();
return this;
}
/**
*
* Uses the FakeQuantWithMinMaxArgs.num_bits attribute to adjust quantized
* array data types throughout the graph. The graph must be properly annotated
* with FakeQuant* ops on at least the edges and may contain additional ops on
* the interior of the graph to widen/narrow as desired.
* Input and output array data types may change because of this propagation
* and users must be sure to query the final data_type values.
*
*
* optional bool propagate_fake_quant_num_bits = 14;
*/
public Builder clearPropagateFakeQuantNumBits() {
bitField0_ = (bitField0_ & ~0x00002000);
propagateFakeQuantNumBits_ = false;
onChanged();
return this;
}
private boolean allowNudgingWeightsToUseFastGemmKernel_ ;
/**
*
* Some fast uint8 GEMM kernels require uint8 weights to avoid the value 0.
* This flag allows nudging them to 1 to allow proceeding, with moderate
* inaccuracy.
*
*
* optional bool allow_nudging_weights_to_use_fast_gemm_kernel = 17;
*/
public boolean hasAllowNudgingWeightsToUseFastGemmKernel() {
return ((bitField0_ & 0x00004000) == 0x00004000);
}
/**
*
* Some fast uint8 GEMM kernels require uint8 weights to avoid the value 0.
* This flag allows nudging them to 1 to allow proceeding, with moderate
* inaccuracy.
*
*
* optional bool allow_nudging_weights_to_use_fast_gemm_kernel = 17;
*/
public boolean getAllowNudgingWeightsToUseFastGemmKernel() {
return allowNudgingWeightsToUseFastGemmKernel_;
}
/**
*
* Some fast uint8 GEMM kernels require uint8 weights to avoid the value 0.
* This flag allows nudging them to 1 to allow proceeding, with moderate
* inaccuracy.
*
*
* optional bool allow_nudging_weights_to_use_fast_gemm_kernel = 17;
*/
public Builder setAllowNudgingWeightsToUseFastGemmKernel(boolean value) {
bitField0_ |= 0x00004000;
allowNudgingWeightsToUseFastGemmKernel_ = value;
onChanged();
return this;
}
/**
*
* Some fast uint8 GEMM kernels require uint8 weights to avoid the value 0.
* This flag allows nudging them to 1 to allow proceeding, with moderate
* inaccuracy.
*
*
* optional bool allow_nudging_weights_to_use_fast_gemm_kernel = 17;
*/
public Builder clearAllowNudgingWeightsToUseFastGemmKernel() {
bitField0_ = (bitField0_ & ~0x00004000);
allowNudgingWeightsToUseFastGemmKernel_ = false;
onChanged();
return this;
}
private long dedupeArrayMinSizeBytes_ = 64L;
/**
*
* Minimum size of constant arrays to deduplicate; arrays smaller will not be
* deduplicated.
*
*
* optional int64 dedupe_array_min_size_bytes = 18 [default = 64];
*/
public boolean hasDedupeArrayMinSizeBytes() {
return ((bitField0_ & 0x00008000) == 0x00008000);
}
/**
*
* Minimum size of constant arrays to deduplicate; arrays smaller will not be
* deduplicated.
*
*
* optional int64 dedupe_array_min_size_bytes = 18 [default = 64];
*/
public long getDedupeArrayMinSizeBytes() {
return dedupeArrayMinSizeBytes_;
}
/**
*
* Minimum size of constant arrays to deduplicate; arrays smaller will not be
* deduplicated.
*
*
* optional int64 dedupe_array_min_size_bytes = 18 [default = 64];
*/
public Builder setDedupeArrayMinSizeBytes(long value) {
bitField0_ |= 0x00008000;
dedupeArrayMinSizeBytes_ = value;
onChanged();
return this;
}
/**
*
* Minimum size of constant arrays to deduplicate; arrays smaller will not be
* deduplicated.
*
*
* optional int64 dedupe_array_min_size_bytes = 18 [default = 64];
*/
public Builder clearDedupeArrayMinSizeBytes() {
bitField0_ = (bitField0_ & ~0x00008000);
dedupeArrayMinSizeBytes_ = 64L;
onChanged();
return this;
}
private boolean splitTfliteLstmInputs_ = true;
/**
*
* Split the LSTM inputs from 5 tensors to 18 tensors for TFLite.
* Ignored if the output format is not TFLite.
*
*
* optional bool split_tflite_lstm_inputs = 19 [default = true];
*/
public boolean hasSplitTfliteLstmInputs() {
return ((bitField0_ & 0x00010000) == 0x00010000);
}
/**
*
* Split the LSTM inputs from 5 tensors to 18 tensors for TFLite.
* Ignored if the output format is not TFLite.
*
*
* optional bool split_tflite_lstm_inputs = 19 [default = true];
*/
public boolean getSplitTfliteLstmInputs() {
return splitTfliteLstmInputs_;
}
/**
*
* Split the LSTM inputs from 5 tensors to 18 tensors for TFLite.
* Ignored if the output format is not TFLite.
*
*
* optional bool split_tflite_lstm_inputs = 19 [default = true];
*/
public Builder setSplitTfliteLstmInputs(boolean value) {
bitField0_ |= 0x00010000;
splitTfliteLstmInputs_ = value;
onChanged();
return this;
}
/**
*
* Split the LSTM inputs from 5 tensors to 18 tensors for TFLite.
* Ignored if the output format is not TFLite.
*
*
* optional bool split_tflite_lstm_inputs = 19 [default = true];
*/
public Builder clearSplitTfliteLstmInputs() {
bitField0_ = (bitField0_ & ~0x00010000);
splitTfliteLstmInputs_ = true;
onChanged();
return this;
}
private boolean quantizeWeights_ ;
/**
*
* Store weights as quantized weights followed by dequantize operations.
* Computation is still done in float, but reduces model size (at the cost of
* accuracy and latency).
* DEPRECATED: Please use post_training_quantize instead.
*
*
* optional bool quantize_weights = 20 [default = false];
*/
public boolean hasQuantizeWeights() {
return ((bitField0_ & 0x00020000) == 0x00020000);
}
/**
*
* Store weights as quantized weights followed by dequantize operations.
* Computation is still done in float, but reduces model size (at the cost of
* accuracy and latency).
* DEPRECATED: Please use post_training_quantize instead.
*
*
* optional bool quantize_weights = 20 [default = false];
*/
public boolean getQuantizeWeights() {
return quantizeWeights_;
}
/**
*
* Store weights as quantized weights followed by dequantize operations.
* Computation is still done in float, but reduces model size (at the cost of
* accuracy and latency).
* DEPRECATED: Please use post_training_quantize instead.
*
*
* optional bool quantize_weights = 20 [default = false];
*/
public Builder setQuantizeWeights(boolean value) {
bitField0_ |= 0x00020000;
quantizeWeights_ = value;
onChanged();
return this;
}
/**
*
* Store weights as quantized weights followed by dequantize operations.
* Computation is still done in float, but reduces model size (at the cost of
* accuracy and latency).
* DEPRECATED: Please use post_training_quantize instead.
*
*
* optional bool quantize_weights = 20 [default = false];
*/
public Builder clearQuantizeWeights() {
bitField0_ = (bitField0_ & ~0x00020000);
quantizeWeights_ = false;
onChanged();
return this;
}
private java.lang.Object dumpGraphvizDir_ = "";
/**
*
* Full filepath of folder to dump the graphs at various stages of processing
* GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order
* to keep the requirements of the output file.
*
*
* optional string dump_graphviz_dir = 24;
*/
public boolean hasDumpGraphvizDir() {
return ((bitField0_ & 0x00040000) == 0x00040000);
}
/**
*
* Full filepath of folder to dump the graphs at various stages of processing
* GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order
* to keep the requirements of the output file.
*
*
* optional string dump_graphviz_dir = 24;
*/
public java.lang.String getDumpGraphvizDir() {
java.lang.Object ref = dumpGraphvizDir_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs =
(com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
if (bs.isValidUtf8()) {
dumpGraphvizDir_ = s;
}
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
* Full filepath of folder to dump the graphs at various stages of processing
* GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order
* to keep the requirements of the output file.
*
*
* optional string dump_graphviz_dir = 24;
*/
public com.google.protobuf.ByteString
getDumpGraphvizDirBytes() {
java.lang.Object ref = dumpGraphvizDir_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8(
(java.lang.String) ref);
dumpGraphvizDir_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
* Full filepath of folder to dump the graphs at various stages of processing
* GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order
* to keep the requirements of the output file.
*
*
* optional string dump_graphviz_dir = 24;
*/
public Builder setDumpGraphvizDir(
java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
bitField0_ |= 0x00040000;
dumpGraphvizDir_ = value;
onChanged();
return this;
}
/**
*
* Full filepath of folder to dump the graphs at various stages of processing
* GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order
* to keep the requirements of the output file.
*
*
* optional string dump_graphviz_dir = 24;
*/
public Builder clearDumpGraphvizDir() {
bitField0_ = (bitField0_ & ~0x00040000);
dumpGraphvizDir_ = getDefaultInstance().getDumpGraphvizDir();
onChanged();
return this;
}
/**
*
* Full filepath of folder to dump the graphs at various stages of processing
* GraphViz .dot files. Preferred over --output_format=GRAPHVIZ_DOT in order
* to keep the requirements of the output file.
*
*
* optional string dump_graphviz_dir = 24;
*/
public Builder setDumpGraphvizDirBytes(
com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
bitField0_ |= 0x00040000;
dumpGraphvizDir_ = value;
onChanged();
return this;
}
private boolean dumpGraphvizIncludeVideo_ ;
/**
*
* Boolean indicating whether to dump the graph after every graph
* transformation.
*
*
* optional bool dump_graphviz_include_video = 25;
*/
public boolean hasDumpGraphvizIncludeVideo() {
return ((bitField0_ & 0x00080000) == 0x00080000);
}
/**
*
* Boolean indicating whether to dump the graph after every graph
* transformation.
*
*
* optional bool dump_graphviz_include_video = 25;
*/
public boolean getDumpGraphvizIncludeVideo() {
return dumpGraphvizIncludeVideo_;
}
/**
*
* Boolean indicating whether to dump the graph after every graph
* transformation.
*
*
* optional bool dump_graphviz_include_video = 25;
*/
public Builder setDumpGraphvizIncludeVideo(boolean value) {
bitField0_ |= 0x00080000;
dumpGraphvizIncludeVideo_ = value;
onChanged();
return this;
}
/**
*
* Boolean indicating whether to dump the graph after every graph
* transformation.
*
*
* optional bool dump_graphviz_include_video = 25;
*/
public Builder clearDumpGraphvizIncludeVideo() {
bitField0_ = (bitField0_ & ~0x00080000);
dumpGraphvizIncludeVideo_ = false;
onChanged();
return this;
}
private boolean postTrainingQuantize_ ;
/**
*
* Boolean indicating whether to quantize the weights of the converted float
* model. Model size will be reduced and there will be latency improvements
* (at the cost of accuracy).
*
*
* optional bool post_training_quantize = 26 [default = false];
*/
public boolean hasPostTrainingQuantize() {
return ((bitField0_ & 0x00100000) == 0x00100000);
}
/**
*
* Boolean indicating whether to quantize the weights of the converted float
* model. Model size will be reduced and there will be latency improvements
* (at the cost of accuracy).
*
*
* optional bool post_training_quantize = 26 [default = false];
*/
public boolean getPostTrainingQuantize() {
return postTrainingQuantize_;
}
/**
*
* Boolean indicating whether to quantize the weights of the converted float
* model. Model size will be reduced and there will be latency improvements
* (at the cost of accuracy).
*
*
* optional bool post_training_quantize = 26 [default = false];
*/
public Builder setPostTrainingQuantize(boolean value) {
bitField0_ |= 0x00100000;
postTrainingQuantize_ = value;
onChanged();
return this;
}
/**
*
* Boolean indicating whether to quantize the weights of the converted float
* model. Model size will be reduced and there will be latency improvements
* (at the cost of accuracy).
*
*
* optional bool post_training_quantize = 26 [default = false];
*/
public Builder clearPostTrainingQuantize() {
bitField0_ = (bitField0_ & ~0x00100000);
postTrainingQuantize_ = false;
onChanged();
return this;
}
private boolean allowFlexOps_ ;
/**
*
* When enabled, unsupported ops will be converted to TFLite Flex ops.
* TODO(ycling): Consider to rename the following 2 flags and don't call it
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* `allow_flex_ops` should always be used with `allow_custom_ops`.
* WARNING: Experimental interface, subject to change
*
*
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*/
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/**
*
* When enabled, unsupported ops will be converted to TFLite Flex ops.
* TODO(ycling): Consider to rename the following 2 flags and don't call it
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* `allow_flex_ops` should always be used with `allow_custom_ops`.
* WARNING: Experimental interface, subject to change
*
*
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*/
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return allowFlexOps_;
}
/**
*
* When enabled, unsupported ops will be converted to TFLite Flex ops.
* TODO(ycling): Consider to rename the following 2 flags and don't call it
* "Flex".
* `allow_flex_ops` should always be used with `allow_custom_ops`.
* WARNING: Experimental interface, subject to change
*
*
* optional bool allow_flex_ops = 27 [default = false];
*/
public Builder setAllowFlexOps(boolean value) {
bitField0_ |= 0x00200000;
allowFlexOps_ = value;
onChanged();
return this;
}
/**
*
* When enabled, unsupported ops will be converted to TFLite Flex ops.
* TODO(ycling): Consider to rename the following 2 flags and don't call it
* "Flex".
* `allow_flex_ops` should always be used with `allow_custom_ops`.
* WARNING: Experimental interface, subject to change
*
*
* optional bool allow_flex_ops = 27 [default = false];
*/
public Builder clearAllowFlexOps() {
bitField0_ = (bitField0_ & ~0x00200000);
allowFlexOps_ = false;
onChanged();
return this;
}
private boolean forceFlexOps_ ;
/**
*
* When enabled, all TensorFlow ops will be converted to TFLite Flex
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* `force_flex_ops` should always be used with `allow_flex_ops`.
* WARNING: Experimental interface, subject to change
*
*
* optional bool force_flex_ops = 28 [default = false];
*/
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return ((bitField0_ & 0x00400000) == 0x00400000);
}
/**
*
* When enabled, all TensorFlow ops will be converted to TFLite Flex
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* `force_flex_ops` should always be used with `allow_flex_ops`.
* WARNING: Experimental interface, subject to change
*
*
* optional bool force_flex_ops = 28 [default = false];
*/
public boolean getForceFlexOps() {
return forceFlexOps_;
}
/**
*
* When enabled, all TensorFlow ops will be converted to TFLite Flex
* ops directly. This will force `allow_flex_ops` to true.
* `force_flex_ops` should always be used with `allow_flex_ops`.
* WARNING: Experimental interface, subject to change
*
*
* optional bool force_flex_ops = 28 [default = false];
*/
public Builder setForceFlexOps(boolean value) {
bitField0_ |= 0x00400000;
forceFlexOps_ = value;
onChanged();
return this;
}
/**
*
* When enabled, all TensorFlow ops will be converted to TFLite Flex
* ops directly. This will force `allow_flex_ops` to true.
* `force_flex_ops` should always be used with `allow_flex_ops`.
* WARNING: Experimental interface, subject to change
*
*
* optional bool force_flex_ops = 28 [default = false];
*/
public Builder clearForceFlexOps() {
bitField0_ = (bitField0_ & ~0x00400000);
forceFlexOps_ = false;
onChanged();
return this;
}
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