Interface OnnxMl.SparseTensorProtoOrBuilder
- All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder,com.google.protobuf.MessageOrBuilder
- All Known Implementing Classes:
OnnxMl.SparseTensorProto,OnnxMl.SparseTensorProto.Builder
- Enclosing class:
OnnxMl
public static interface OnnxMl.SparseTensorProtoOrBuilder
extends com.google.protobuf.MessageOrBuilder
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Method Summary
Modifier and TypeMethodDescriptionlonggetDims(int index) The shape of the underlying dense-tensor: [dim_1, dim_2, ...intThe shape of the underlying dense-tensor: [dim_1, dim_2, ...The shape of the underlying dense-tensor: [dim_1, dim_2, ...The indices of the non-default values, which may be stored in one of two formats.The indices of the non-default values, which may be stored in one of two formats.The sequence of non-default values are encoded as a tensor of shape [NNZ].The sequence of non-default values are encoded as a tensor of shape [NNZ].booleanThe indices of the non-default values, which may be stored in one of two formats.booleanThe sequence of non-default values are encoded as a tensor of shape [NNZ].Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder
isInitializedMethods inherited from interface com.google.protobuf.MessageOrBuilder
findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof
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Method Details
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hasValues
boolean hasValues()The sequence of non-default values are encoded as a tensor of shape [NNZ]. The default-value is zero for numeric tensors, and empty-string for string tensors. values must have a non-empty name present which serves as a name for SparseTensorProto when used in sparse_initializer list.
optional .onnx.TensorProto values = 1;- Returns:
- Whether the values field is set.
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getValues
OnnxMl.TensorProto getValues()The sequence of non-default values are encoded as a tensor of shape [NNZ]. The default-value is zero for numeric tensors, and empty-string for string tensors. values must have a non-empty name present which serves as a name for SparseTensorProto when used in sparse_initializer list.
optional .onnx.TensorProto values = 1;- Returns:
- The values.
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getValuesOrBuilder
OnnxMl.TensorProtoOrBuilder getValuesOrBuilder()The sequence of non-default values are encoded as a tensor of shape [NNZ]. The default-value is zero for numeric tensors, and empty-string for string tensors. values must have a non-empty name present which serves as a name for SparseTensorProto when used in sparse_initializer list.
optional .onnx.TensorProto values = 1; -
hasIndices
boolean hasIndices()The indices of the non-default values, which may be stored in one of two formats. (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value corresponding to the j-th index of the i-th value (in the values tensor). (b) Indices can be a tensor of shape [NNZ], in which case the i-th value must be the linearized-index of the i-th value (in the values tensor). The linearized-index can be converted into an index tuple (k_1,...,k_rank) using the shape provided below. The indices must appear in ascending order without duplication. In the first format, the ordering is lexicographic-ordering: e.g., index-value [1,4] must appear before [2,1]
optional .onnx.TensorProto indices = 2;- Returns:
- Whether the indices field is set.
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getIndices
OnnxMl.TensorProto getIndices()The indices of the non-default values, which may be stored in one of two formats. (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value corresponding to the j-th index of the i-th value (in the values tensor). (b) Indices can be a tensor of shape [NNZ], in which case the i-th value must be the linearized-index of the i-th value (in the values tensor). The linearized-index can be converted into an index tuple (k_1,...,k_rank) using the shape provided below. The indices must appear in ascending order without duplication. In the first format, the ordering is lexicographic-ordering: e.g., index-value [1,4] must appear before [2,1]
optional .onnx.TensorProto indices = 2;- Returns:
- The indices.
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getIndicesOrBuilder
OnnxMl.TensorProtoOrBuilder getIndicesOrBuilder()The indices of the non-default values, which may be stored in one of two formats. (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value corresponding to the j-th index of the i-th value (in the values tensor). (b) Indices can be a tensor of shape [NNZ], in which case the i-th value must be the linearized-index of the i-th value (in the values tensor). The linearized-index can be converted into an index tuple (k_1,...,k_rank) using the shape provided below. The indices must appear in ascending order without duplication. In the first format, the ordering is lexicographic-ordering: e.g., index-value [1,4] must appear before [2,1]
optional .onnx.TensorProto indices = 2; -
getDimsList
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getDimsCount
int getDimsCount()The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
repeated int64 dims = 3;- Returns:
- The count of dims.
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getDims
long getDims(int index) The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
repeated int64 dims = 3;- Parameters:
index- The index of the element to return.- Returns:
- The dims at the given index.
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