Package ai.onnx.proto

Interface OnnxMl.ModelProtoOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
OnnxMl.ModelProto, OnnxMl.ModelProto.Builder
Enclosing class:
OnnxMl
public static interface OnnxMl.ModelProtoOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Details

    • hasIrVersion

      boolean hasIrVersion()
       The version of the IR this model targets. See Version enum above.
 This field MUST be present.
      optional int64 ir_version = 1;
      Returns:
      Whether the irVersion field is set.

    • getIrVersion

      long getIrVersion()
       The version of the IR this model targets. See Version enum above.
 This field MUST be present.
      optional int64 ir_version = 1;
      Returns:
      The irVersion.

    • getOpsetImportList

      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;

    • getOpsetImport

      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;

    • getOpsetImportCount

      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;

    • getOpsetImportOrBuilderList

      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;

    • getOpsetImportOrBuilder

      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;

    • hasProducerName

      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;
      Returns:
      Whether the producerName field is set.

    • getProducerName

      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;
      Returns:
      The producerName.

    • getProducerNameBytes

      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;
      Returns:
      The bytes for producerName.

    • hasProducerVersion

      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;
      Returns:
      Whether the producerVersion field is set.

    • getProducerVersion

      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;
      Returns:
      The producerVersion.

    • getProducerVersionBytes

      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;
      Returns:
      The bytes for producerVersion.

    • hasDomain

      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;
      Returns:
      Whether the domain field is set.

    • getDomain

      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;
      Returns:
      The domain.

    • getDomainBytes

      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;
      Returns:
      The bytes for domain.

    • hasModelVersion

      boolean hasModelVersion()
       The version of the graph encoded. See Version enum below.
      optional int64 model_version = 5;
      Returns:
      Whether the modelVersion field is set.

    • getModelVersion

      long getModelVersion()
       The version of the graph encoded. See Version enum below.
      optional int64 model_version = 5;
      Returns:
      The modelVersion.

    • hasDocString

      boolean hasDocString()
       A human-readable documentation for this model. Markdown is allowed.
      optional string doc_string = 6;
      Returns:
      Whether the docString field is set.

    • getDocString

      String getDocString()
       A human-readable documentation for this model. Markdown is allowed.
      optional string doc_string = 6;
      Returns:
      The docString.

    • getDocStringBytes

      com.google.protobuf.ByteString getDocStringBytes()
       A human-readable documentation for this model. Markdown is allowed.
      optional string doc_string = 6;
      Returns:
      The bytes for docString.

    • hasGraph

      boolean hasGraph()
       The parameterized graph that is evaluated to execute the model.
      optional .onnx.GraphProto graph = 7;
      Returns:
      Whether the graph field is set.

    • getGraph

      OnnxMl.GraphProto getGraph()
       The parameterized graph that is evaluated to execute the model.
      optional .onnx.GraphProto graph = 7;
      Returns:
      The graph.

    • getGraphOrBuilder

      OnnxMl.GraphProtoOrBuilder getGraphOrBuilder()
       The parameterized graph that is evaluated to execute the model.
      optional .onnx.GraphProto graph = 7;

    • getMetadataPropsList

      List<OnnxMl.StringStringEntryProto> getMetadataPropsList()
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • getMetadataProps

      OnnxMl.StringStringEntryProto getMetadataProps(int index)
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • getMetadataPropsCount

      int getMetadataPropsCount()
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • getMetadataPropsOrBuilderList

      List<? extends OnnxMl.StringStringEntryProtoOrBuilder> getMetadataPropsOrBuilderList()
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • getMetadataPropsOrBuilder

      OnnxMl.StringStringEntryProtoOrBuilder getMetadataPropsOrBuilder(int index)
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • getTrainingInfoList

      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;

    • getTrainingInfo

      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;

    • getTrainingInfoCount

      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;

    • getTrainingInfoOrBuilderList

      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;

    • getTrainingInfoOrBuilder

      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;