public static interface OnnxMl.ModelProtoOrBuilder
extends com.google.protobuf.MessageOrBuilder
| Modifier and Type | Method and Description |
|---|---|
java.lang.String |
getDocString()
A human-readable documentation for this model.
|
com.google.protobuf.ByteString |
getDocStringBytes()
A human-readable documentation for this model.
|
java.lang.String |
getDomain()
Domain name of the model.
|
com.google.protobuf.ByteString |
getDomainBytes()
Domain name of the model.
|
OnnxMl.GraphProto |
getGraph()
The parameterized graph that is evaluated to execute the model.
|
OnnxMl.GraphProtoOrBuilder |
getGraphOrBuilder()
The parameterized graph that is evaluated to execute the model.
|
long |
getIrVersion()
The version of the IR this model targets.
|
OnnxMl.StringStringEntryProto |
getMetadataProps(int index)
Named metadata values; keys should be distinct.
|
int |
getMetadataPropsCount()
Named metadata values; keys should be distinct.
|
java.util.List<OnnxMl.StringStringEntryProto> |
getMetadataPropsList()
Named metadata values; keys should be distinct.
|
OnnxMl.StringStringEntryProtoOrBuilder |
getMetadataPropsOrBuilder(int index)
Named metadata values; keys should be distinct.
|
java.util.List<? extends OnnxMl.StringStringEntryProtoOrBuilder> |
getMetadataPropsOrBuilderList()
Named metadata values; keys should be distinct.
|
long |
getModelVersion()
The version of the graph encoded.
|
OnnxMl.OperatorSetIdProto |
getOpsetImport(int index)
The OperatorSets this model relies on.
|
int |
getOpsetImportCount()
The OperatorSets this model relies on.
|
java.util.List<OnnxMl.OperatorSetIdProto> |
getOpsetImportList()
The OperatorSets this model relies on.
|
OnnxMl.OperatorSetIdProtoOrBuilder |
getOpsetImportOrBuilder(int index)
The OperatorSets this model relies on.
|
java.util.List<? extends OnnxMl.OperatorSetIdProtoOrBuilder> |
getOpsetImportOrBuilderList()
The OperatorSets this model relies on.
|
java.lang.String |
getProducerName()
The name of the framework or tool used to generate this model.
|
com.google.protobuf.ByteString |
getProducerNameBytes()
The name of the framework or tool used to generate this model.
|
java.lang.String |
getProducerVersion()
The version of the framework or tool used to generate this model.
|
com.google.protobuf.ByteString |
getProducerVersionBytes()
The version of the framework or tool used to generate this model.
|
OnnxMl.TrainingInfoProto |
getTrainingInfo(int index)
Training-specific information.
|
int |
getTrainingInfoCount()
Training-specific information.
|
java.util.List<OnnxMl.TrainingInfoProto> |
getTrainingInfoList()
Training-specific information.
|
OnnxMl.TrainingInfoProtoOrBuilder |
getTrainingInfoOrBuilder(int index)
Training-specific information.
|
java.util.List<? extends OnnxMl.TrainingInfoProtoOrBuilder> |
getTrainingInfoOrBuilderList()
Training-specific information.
|
boolean |
hasDocString()
A human-readable documentation for this model.
|
boolean |
hasDomain()
Domain name of the model.
|
boolean |
hasGraph()
The parameterized graph that is evaluated to execute the model.
|
boolean |
hasIrVersion()
The version of the IR this model targets.
|
boolean |
hasModelVersion()
The version of the graph encoded.
|
boolean |
hasProducerName()
The name of the framework or tool used to generate this model.
|
boolean |
hasProducerVersion()
The version of the framework or tool used to generate this model.
|
findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneofboolean hasIrVersion()
The version of the IR this model targets. See Version enum above. This field MUST be present.
optional int64 ir_version = 1;long getIrVersion()
The version of the IR this model targets. See Version enum above. This field MUST be present.
optional int64 ir_version = 1;java.util.List<OnnxMl.OperatorSetIdProto> getOpsetImportList()
The OperatorSets this model relies on. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. All nodes in the ModelProto's graph will bind against the operator with the same-domain/same-op_type operator with the HIGHEST version in the referenced operator sets.
repeated .onnx.OperatorSetIdProto opset_import = 8;OnnxMl.OperatorSetIdProto getOpsetImport(int index)
The OperatorSets this model relies on. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. All nodes in the ModelProto's graph will bind against the operator with the same-domain/same-op_type operator with the HIGHEST version in the referenced operator sets.
repeated .onnx.OperatorSetIdProto opset_import = 8;int getOpsetImportCount()
The OperatorSets this model relies on. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. All nodes in the ModelProto's graph will bind against the operator with the same-domain/same-op_type operator with the HIGHEST version in the referenced operator sets.
repeated .onnx.OperatorSetIdProto opset_import = 8;java.util.List<? extends OnnxMl.OperatorSetIdProtoOrBuilder> getOpsetImportOrBuilderList()
The OperatorSets this model relies on. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. All nodes in the ModelProto's graph will bind against the operator with the same-domain/same-op_type operator with the HIGHEST version in the referenced operator sets.
repeated .onnx.OperatorSetIdProto opset_import = 8;OnnxMl.OperatorSetIdProtoOrBuilder getOpsetImportOrBuilder(int index)
The OperatorSets this model relies on. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. All nodes in the ModelProto's graph will bind against the operator with the same-domain/same-op_type operator with the HIGHEST version in the referenced operator sets.
repeated .onnx.OperatorSetIdProto opset_import = 8;boolean hasProducerName()
The name of the framework or tool used to generate this model. This field SHOULD be present to indicate which implementation/tool/framework emitted the model.
optional string producer_name = 2;java.lang.String getProducerName()
The name of the framework or tool used to generate this model. This field SHOULD be present to indicate which implementation/tool/framework emitted the model.
optional string producer_name = 2;com.google.protobuf.ByteString getProducerNameBytes()
The name of the framework or tool used to generate this model. This field SHOULD be present to indicate which implementation/tool/framework emitted the model.
optional string producer_name = 2;boolean hasProducerVersion()
The version of the framework or tool used to generate this model. This field SHOULD be present to indicate which implementation/tool/framework emitted the model.
optional string producer_version = 3;java.lang.String getProducerVersion()
The version of the framework or tool used to generate this model. This field SHOULD be present to indicate which implementation/tool/framework emitted the model.
optional string producer_version = 3;com.google.protobuf.ByteString getProducerVersionBytes()
The version of the framework or tool used to generate this model. This field SHOULD be present to indicate which implementation/tool/framework emitted the model.
optional string producer_version = 3;boolean hasDomain()
Domain name of the model. We use reverse domain names as name space indicators. For example: `com.facebook.fair` or `com.microsoft.cognitiveservices` Together with `model_version` and GraphProto.name, this forms the unique identity of the graph.
optional string domain = 4;java.lang.String getDomain()
Domain name of the model. We use reverse domain names as name space indicators. For example: `com.facebook.fair` or `com.microsoft.cognitiveservices` Together with `model_version` and GraphProto.name, this forms the unique identity of the graph.
optional string domain = 4;com.google.protobuf.ByteString getDomainBytes()
Domain name of the model. We use reverse domain names as name space indicators. For example: `com.facebook.fair` or `com.microsoft.cognitiveservices` Together with `model_version` and GraphProto.name, this forms the unique identity of the graph.
optional string domain = 4;boolean hasModelVersion()
The version of the graph encoded. See Version enum below.
optional int64 model_version = 5;long getModelVersion()
The version of the graph encoded. See Version enum below.
optional int64 model_version = 5;boolean hasDocString()
A human-readable documentation for this model. Markdown is allowed.
optional string doc_string = 6;java.lang.String getDocString()
A human-readable documentation for this model. Markdown is allowed.
optional string doc_string = 6;com.google.protobuf.ByteString getDocStringBytes()
A human-readable documentation for this model. Markdown is allowed.
optional string doc_string = 6;boolean hasGraph()
The parameterized graph that is evaluated to execute the model.
optional .onnx.GraphProto graph = 7;OnnxMl.GraphProto getGraph()
The parameterized graph that is evaluated to execute the model.
optional .onnx.GraphProto graph = 7;OnnxMl.GraphProtoOrBuilder getGraphOrBuilder()
The parameterized graph that is evaluated to execute the model.
optional .onnx.GraphProto graph = 7;java.util.List<OnnxMl.StringStringEntryProto> getMetadataPropsList()
Named metadata values; keys should be distinct.
repeated .onnx.StringStringEntryProto metadata_props = 14;OnnxMl.StringStringEntryProto getMetadataProps(int index)
Named metadata values; keys should be distinct.
repeated .onnx.StringStringEntryProto metadata_props = 14;int getMetadataPropsCount()
Named metadata values; keys should be distinct.
repeated .onnx.StringStringEntryProto metadata_props = 14;java.util.List<? extends OnnxMl.StringStringEntryProtoOrBuilder> getMetadataPropsOrBuilderList()
Named metadata values; keys should be distinct.
repeated .onnx.StringStringEntryProto metadata_props = 14;OnnxMl.StringStringEntryProtoOrBuilder getMetadataPropsOrBuilder(int index)
Named metadata values; keys should be distinct.
repeated .onnx.StringStringEntryProto metadata_props = 14;java.util.List<OnnxMl.TrainingInfoProto> getTrainingInfoList()
Training-specific information. Sequentially executing all stored `TrainingInfoProto.algorithm`s and assigning their outputs following the corresponding `TrainingInfoProto.update_binding`s is one training iteration. Similarly, to initialize the model (as if training hasn't happened), the user should sequentially execute all stored `TrainingInfoProto.initialization`s and assigns their outputs using `TrainingInfoProto.initialization_binding`s. If this field is empty, the training behavior of the model is undefined.
repeated .onnx.TrainingInfoProto training_info = 20;OnnxMl.TrainingInfoProto getTrainingInfo(int index)
Training-specific information. Sequentially executing all stored `TrainingInfoProto.algorithm`s and assigning their outputs following the corresponding `TrainingInfoProto.update_binding`s is one training iteration. Similarly, to initialize the model (as if training hasn't happened), the user should sequentially execute all stored `TrainingInfoProto.initialization`s and assigns their outputs using `TrainingInfoProto.initialization_binding`s. If this field is empty, the training behavior of the model is undefined.
repeated .onnx.TrainingInfoProto training_info = 20;int getTrainingInfoCount()
Training-specific information. Sequentially executing all stored `TrainingInfoProto.algorithm`s and assigning their outputs following the corresponding `TrainingInfoProto.update_binding`s is one training iteration. Similarly, to initialize the model (as if training hasn't happened), the user should sequentially execute all stored `TrainingInfoProto.initialization`s and assigns their outputs using `TrainingInfoProto.initialization_binding`s. If this field is empty, the training behavior of the model is undefined.
repeated .onnx.TrainingInfoProto training_info = 20;java.util.List<? extends OnnxMl.TrainingInfoProtoOrBuilder> getTrainingInfoOrBuilderList()
Training-specific information. Sequentially executing all stored `TrainingInfoProto.algorithm`s and assigning their outputs following the corresponding `TrainingInfoProto.update_binding`s is one training iteration. Similarly, to initialize the model (as if training hasn't happened), the user should sequentially execute all stored `TrainingInfoProto.initialization`s and assigns their outputs using `TrainingInfoProto.initialization_binding`s. If this field is empty, the training behavior of the model is undefined.
repeated .onnx.TrainingInfoProto training_info = 20;OnnxMl.TrainingInfoProtoOrBuilder getTrainingInfoOrBuilder(int index)
Training-specific information. Sequentially executing all stored `TrainingInfoProto.algorithm`s and assigning their outputs following the corresponding `TrainingInfoProto.update_binding`s is one training iteration. Similarly, to initialize the model (as if training hasn't happened), the user should sequentially execute all stored `TrainingInfoProto.initialization`s and assigns their outputs using `TrainingInfoProto.initialization_binding`s. If this field is empty, the training behavior of the model is undefined.
repeated .onnx.TrainingInfoProto training_info = 20;