Struct rerun::sdk::prelude::Tensor

pub struct Tensor {
    pub data: Option<SerializedComponentBatch>,
    pub value_range: Option<SerializedComponentBatch>,
}

Archetype: An N-dimensional array of numbers.

Example

Simple tensor

use ndarray::{Array, ShapeBuilder};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let rec = rerun::RecordingStreamBuilder::new("rerun_example_tensor").spawn()?;

    let mut data = Array::<u8, _>::default((8, 6, 3, 5).f());
    data.map_inplace(|x| *x = rand::random());

    let tensor =
        rerun::Tensor::try_from(data)?.with_dim_names(["width", "height", "channel", "batch"]);
    rec.log("tensor", &tensor)?;

    Ok(())
}

Fields

data: Option<SerializedComponentBatch>

The tensor data

value_range: Option<SerializedComponentBatch>

The expected range of values.

This is typically the expected range of valid values. Everything outside of the range is clamped to the range for the purpose of colormpaping. Any colormap applied for display, will map this range.

If not specified, the range will be automatically estimated from the data. Note that the Viewer may try to guess a wider range than the minimum/maximum of values in the contents of the tensor. E.g. if all values are positive, some bigger than 1.0 and all smaller than 255.0, the Viewer will guess that the data likely came from an 8bit image, thus assuming a range of 0-255.

Implementations

impl Tensor

pub fn descriptor_data() -> ComponentDescriptor

Returns the ComponentDescriptor for Self::data.

pub fn descriptor_value_range() -> ComponentDescriptor

Returns the ComponentDescriptor for Self::value_range.

pub fn descriptor_indicator() -> ComponentDescriptor

Returns the ComponentDescriptor for the associated indicator component.

impl Tensor

pub const NUM_COMPONENTS: usize = 3usize

The total number of components in the archetype: 1 required, 1 recommended, 1 optional

impl Tensor

pub fn new(data: impl Into<TensorData>) -> Tensor

Create a new Tensor.

pub fn update_fields() -> Tensor

Update only some specific fields of a Tensor.

pub fn clear_fields() -> Tensor

Clear all the fields of a Tensor.

pub fn columns<I>( self, _lengths: I, ) -> Result<impl Iterator<Item = SerializedComponentColumn>, SerializationError>

where I: IntoIterator<Item = usize> + Clone,

Partitions the component data into multiple sub-batches.

Specifically, this transforms the existing SerializedComponentBatches data into SerializedComponentColumns instead, via SerializedComponentBatch::partitioned.

This makes it possible to use RecordingStream::send_columns to send columnar data directly into Rerun.

The specified lengths must sum to the total length of the component batch.

pub fn columns_of_unit_batches( self, ) -> Result<impl Iterator<Item = SerializedComponentColumn>, SerializationError>

Helper to partition the component data into unit-length sub-batches.

This is semantically similar to calling Self::columns with std::iter::take(1).repeat(n), where n is automatically guessed.

pub fn with_data(self, data: impl Into<TensorData>) -> Tensor

The tensor data

pub fn with_many_data( self, data: impl IntoIterator<Item = impl Into<TensorData>>, ) -> Tensor

This method makes it possible to pack multiple crate::components::TensorData in a single component batch.

This only makes sense when used in conjunction with Self::columns. Self::with_data should be used when logging a single row’s worth of data.

pub fn with_value_range(self, value_range: impl Into<ValueRange>) -> Tensor

The expected range of values.

This is typically the expected range of valid values. Everything outside of the range is clamped to the range for the purpose of colormpaping. Any colormap applied for display, will map this range.

If not specified, the range will be automatically estimated from the data. Note that the Viewer may try to guess a wider range than the minimum/maximum of values in the contents of the tensor. E.g. if all values are positive, some bigger than 1.0 and all smaller than 255.0, the Viewer will guess that the data likely came from an 8bit image, thus assuming a range of 0-255.

pub fn with_many_value_range( self, value_range: impl IntoIterator<Item = impl Into<ValueRange>>, ) -> Tensor

This method makes it possible to pack multiple crate::components::ValueRange in a single component batch.

This only makes sense when used in conjunction with Self::columns. Self::with_value_range should be used when logging a single row’s worth of data.

impl Tensor

pub fn try_from<T>(data: T) -> Result<Tensor, <T as TryInto<TensorData>>::Error>

where T: TryInto<TensorData>,

Try to construct a Tensor from anything that can be converted into TensorData

This is useful for constructing a tensor from an ndarray.

pub fn with_dim_names( self, names: impl IntoIterator<Item = impl Into<ArrowString>>, ) -> Tensor

Update the names of the contained TensorData dimensions.

Any existing names will be overwritten.

If the wrong number of names are given, a warning will be logged, and the names might not show up correctly.

impl Tensor

pub fn from_image( image: impl Into<DynamicImage>, ) -> Result<Tensor, TensorImageLoadError>

Construct a tensor from something that can be turned into a image::DynamicImage.

Requires the image feature.

pub fn from_dynamic_image( image: DynamicImage, ) -> Result<Tensor, TensorImageLoadError>

Construct a tensor from image::DynamicImage.

Requires the image feature.

Trait Implementations

impl Archetype for Tensor

type Indicator = GenericIndicatorComponent<Tensor>

The associated indicator component, whose presence indicates that the high-level archetype-based APIs were used to log the data.

fn name() -> ArchetypeName

The fully-qualified name of this archetype, e.g. rerun.archetypes.Points2D.

fn display_name() -> &'static str

Readable name for displaying in UI.

fn indicator() -> SerializedComponentBatch

Creates a ComponentBatch out of the associated Self::Indicator component.

fn required_components() -> Cow<'static, [ComponentDescriptor]>

Returns all component descriptors that must be provided by the user when constructing this archetype.

fn recommended_components() -> Cow<'static, [ComponentDescriptor]>

Returns all component descriptors that should be provided by the user when constructing this archetype.

fn optional_components() -> Cow<'static, [ComponentDescriptor]>

Returns all component descriptors that may be provided by the user when constructing this archetype.

fn all_components() -> Cow<'static, [ComponentDescriptor]>

Returns all component descriptors that must, should and may be provided by the user when constructing this archetype.

fn from_arrow_components( arrow_data: impl IntoIterator<Item = (ComponentDescriptor, Arc<dyn Array>)>, ) -> Result<Tensor, DeserializationError>

Given an iterator of Arrow arrays and their respective ComponentNames, deserializes them into this archetype.

fn from_arrow( data: impl IntoIterator<Item = (Field, Arc<dyn Array>)>, ) -> Result<Self, DeserializationError>

where Self: Sized,

Given an iterator of Arrow arrays and their respective field metadata, deserializes them into this archetype.

impl AsComponents for Tensor

fn as_serialized_batches(&self) -> Vec<SerializedComponentBatch>

Exposes the object’s contents as a set of SerializedComponentBatches.

fn to_arrow(&self) -> Result<Vec<(Field, Arc<dyn Array>)>, SerializationError>

Serializes all non-null Components of this bundle into Arrow arrays.

impl Clone for Tensor

fn clone(&self) -> Tensor

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for Tensor

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

Formats the value using the given formatter.

impl Default for Tensor

fn default() -> Tensor

Returns the “default value” for a type.

impl PartialEq for Tensor

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

This method tests for self and other values to be equal, and is used by ==.

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

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl SizeBytes for Tensor

fn heap_size_bytes(&self) -> u64

Returns how many bytes self uses on the heap.

fn total_size_bytes(&self) -> u64

Returns the total size of self in bytes, accounting for both stack and heap space.

fn stack_size_bytes(&self) -> u64

Returns the total size of self on the stack, in bytes.

fn is_pod() -> bool

Is Self just plain old data?

impl ArchetypeReflectionMarker for Tensor

impl StructuralPartialEq for Tensor