Struct FixedShapeTensor 

pub struct FixedShapeTensor {
    value_type: DataType,
    metadata: FixedShapeTensorMetadata,
}

The extension type for fixed shape tensor.

Extension name: arrow.fixed_shape_tensor.

The storage type of the extension: FixedSizeList where:

• value_type is the data type of individual tensor elements.
• list_size is the product of all the elements in tensor shape.

Extension type parameters:

• value_type: the Arrow data type of individual tensor elements.
• shape: the physical shape of the contained tensors as an array.

Optional parameters describing the logical layout:

• dim_names: explicit names to tensor dimensions as an array. The length of it should be equal to the shape length and equal to the number of dimensions. dim_names can be used if the dimensions have well-known names and they map to the physical layout (row-major).
• permutation: indices of the desired ordering of the original dimensions, defined as an array. The indices contain a permutation of the values [0, 1, .., N-1] where N is the number of dimensions. The permutation indicates which dimension of the logical layout corresponds to which dimension of the physical tensor (the i-th dimension of the logical view corresponds to the dimension with number permutations[i] of the physical tensor). Permutation can be useful in case the logical order of the tensor is a permutation of the physical order (row-major). When logical and physical layout are equal, the permutation will always be ([0, 1, .., N-1]) and can therefore be left out.

Description of the serialization: The metadata must be a valid JSON object including shape of the contained tensors as an array with key shape plus optional dimension names with keys dim_names and ordering of the dimensions with key permutation. Example: { "shape": [2, 5]} Example with dim_names metadata for NCHW ordered data: { "shape": [100, 200, 500], "dim_names": ["C", "H", "W"]} Example of permuted 3-dimensional tensor: { "shape": [100, 200, 500], "permutation": [2, 0, 1]}

This is the physical layout shape and the shape of the logical layout would in this case be [500, 100, 200].

https://arrow.apache.org/docs/format/CanonicalExtensions.html#fixed-shape-tensor

Fields

value_type: DataType

The data type of individual tensor elements.

metadata: FixedShapeTensorMetadata

The metadata of this extension type.

Implementations

impl FixedShapeTensor

pub fn try_new( value_type: DataType, shape: impl IntoIterator<Item = usize>, dimension_names: Option<Vec<String>>, permutations: Option<Vec<usize>>, ) -> Result<Self, ArrowError>

Returns a new fixed shape tensor extension type.

Error

Return an error if the provided dimension names or permutations are invalid.

pub fn value_type(&self) -> &DataType

Returns the value type of the individual tensor elements.

pub fn list_size(&self) -> usize

Returns the product of all the elements in tensor shape.

pub fn shape(&self) -> &[usize]

Returns the physical shape of the contained tensors.

Self::dimensions and Self::list_size do not identify this shape. [2, 6] and [3, 4] are both 2-dimensional and both have 12 elements.

let tensor = FixedShapeTensor::try_new(DataType::Float32, [2, 6], None, None).unwrap();
assert_eq!(tensor.shape(), &[2, 6]);
assert_eq!(tensor.list_size(), 12);

pub fn dimensions(&self) -> usize

Returns the number of dimensions in this fixed shape tensor.

pub fn dimension_names(&self) -> Option<&[String]>

Returns the names of the dimensions in this fixed shape tensor, if set.

pub fn permutations(&self) -> Option<&[usize]>

Returns the indices of the desired ordering of the original dimensions, if set.

pub fn logical_shape(&self) -> Vec<usize>

Returns the logical shape.

Logical dimension i has the size of physical dimension permutations[i]. Without a permutation, this is the physical shape.

Physical shape [100, 200, 500] and permutation [2, 0, 1] have logical shape [500, 100, 200].

let tensor = FixedShapeTensor::try_new(
    DataType::Float32,
    [100, 200, 500],
    None,
    Some(vec![2, 0, 1]),
)
.unwrap();
assert_eq!(tensor.logical_shape(), vec![500, 100, 200]);

Trait Implementations

impl Clone for FixedShapeTensor

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for FixedShapeTensor

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl ExtensionType for FixedShapeTensor

const NAME: &'static str = "arrow.fixed_shape_tensor"

The name identifying this extension type.

type Metadata = FixedShapeTensorMetadata

The metadata type of this extension type.

fn metadata(&self) -> &Self::Metadata

Returns a reference to the metadata of this extension type, or &() if if this extension type defines no metadata (Self::Metadata=()).

fn serialize_metadata(&self) -> Option<String>

Returns the serialized representation of the metadata of this extension type, or None if this extension type defines no metadata (Self::Metadata=()).

fn deserialize_metadata( metadata: Option<&str>, ) -> Result<Self::Metadata, ArrowError>

Deserialize the metadata of this extension type from the serialized representation of the metadata. An extension type that defines no metadata should expect None for the serialized metadata and return Ok(()).

fn supports_data_type(&self, data_type: &DataType) -> Result<(), ArrowError>

Returns Ok(()) iff the given data type is supported by this extension type.

fn try_new( data_type: &DataType, metadata: Self::Metadata, ) -> Result<Self, ArrowError>

Construct this extension type for a field with the given data type and metadata.

fn validate( data_type: &DataType, metadata: Self::Metadata, ) -> Result<(), ArrowError>

Validate this extension type for a field with the given data type and metadata.

fn try_new_from_field_metadata( data_type: &DataType, metadata: &Metadata, ) -> Result<Self, ArrowError>

Construct this extension type from field metadata and data type.

impl From<FixedShapeTensor> for CanonicalExtensionType

fn from(value: FixedShapeTensor) -> Self

Converts to this type from the input type.

impl PartialEq for FixedShapeTensor

fn eq(&self, other: &Self) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl StructuralPartialEq for FixedShapeTensor