Trait Transform 

pub trait Transform<T>: Send + Sync {
    type Output;

    // Required method
    fn transform(&self, input: T) -> Result<Self::Output>;

    // Provided method
    fn is_deterministic(&self) -> bool { ... }
}

Core transform trait - all transformations must implement this

Required Associated Types

type Output

Required Methods

fn transform(&self, input: T) -> Result<Self::Output>

Apply the transformation to the input

Provided Methods

fn is_deterministic(&self) -> bool

Check if the transform is deterministic

A deterministic transform always produces the same output for the same input. Non-deterministic transforms include random augmentations.

Implementors

impl Transform<DynamicImage> for ImageToTensor

type Output = Tensor

impl Transform<DynamicImage> for torsh_data::vision::image::transforms::RandomHorizontalFlip

type Output = DynamicImage

impl Transform<DynamicImage> for RandomRotation

type Output = DynamicImage

impl Transform<DynamicImage> for torsh_data::vision::image::transforms::RandomVerticalFlip

type Output = DynamicImage

impl Transform<DynamicImage> for torsh_data::vision::image::transforms::transforms::CenterCrop

type Output = DynamicImage

impl Transform<DynamicImage> for torsh_data::vision::image::transforms::transforms::Resize

type Output = DynamicImage

impl Transform<TextSequence> for TokenIdsToTensor

type Output = Tensor

impl Transform<TextSequence> for AddSpecialTokens

type Output = TextSequence

impl Transform<TextSequence> for CharTokenizer

type Output = TextSequence

impl Transform<TextSequence> for FixedLength

type Output = TextSequence

impl Transform<TextSequence> for NGrams

type Output = TextSequence

impl Transform<TextSequence> for torsh_data::text::transforms::RemovePunctuation

type Output = TextSequence

impl Transform<TextSequence> for SimpleBPE

type Output = TextSequence

impl Transform<TextSequence> for torsh_data::text::transforms::ToLowercase

type Output = TextSequence

impl Transform<VideoFrames> for VideoToTensor

type Output = Tensor

impl Transform<String> for ChangeCase

type Output = String

impl Transform<String> for CollapseWhitespace

type Output = String

impl Transform<String> for PorterStemmer

type Output = String

impl Transform<String> for RemoveNumbers

type Output = String

impl Transform<String> for torsh_data::text_processing::RemovePunctuation

type Output = String

impl Transform<String> for ReplacePattern

type Output = String

impl Transform<String> for torsh_data::text_processing::ToLowercase

type Output = String

impl Transform<String> for Tokenize

type Output = Vec<String>

impl Transform<String> for TrimWhitespace

type Output = String

impl Transform<Vec<String>> for FilterByLength

type Output = Vec<String>

impl Transform<Vec<String>> for NGramGenerator

type Output = Vec<String>

impl Transform<Vec<String>> for RemoveStopwords

type Output = Vec<String>

impl Transform<Tensor> for TensorToImage

type Output = DynamicImage

impl Transform<Tensor> for Normalize

type Output = Tensor

impl Transform<Tensor> for TensorToVideo

type Output = VideoFrames

impl<F, T, R> Transform<T> for Lambda<F>

where F: Fn(T) -> Result<R> + Send + Sync,

type Output = R

impl<T> Transform<T> for AugmentationPipeline<T>

type Output = T

impl<T> Transform<T> for Compose<T>

type Output = T

impl<T, F> Transform<T> for ConditionalTransform<T, F>

where F: Transform<T, Output = T>, T: Send + Sync,

type Output = T

impl<T, U, V> Transform<T> for Chain<U, V>

where U: Transform<T>, V: Transform<U::Output>,

type Output = <V as Transform<<U as Transform<T>>::Output>>::Output

impl<T: FloatElement> Transform<Tensor<T>> for GaussianNoise

type Output = Tensor<T>

impl<T: FloatElement> Transform<Tensor<T>> for RandomBrightness

type Output = Tensor<T>

impl<T: FloatElement> Transform<Tensor<T>> for RandomContrast

type Output = Tensor<T>

impl<T: FloatElement> Transform<Tensor<T>> for RandomErasing

type Output = Tensor<T>

impl<T: FloatElement> Transform<Tensor<T>> for RandomHue

type Output = Tensor<T>

impl<T: FloatElement> Transform<Tensor<T>> for RandomSaturation

type Output = Tensor<T>

impl<T: FloatElement> Transform<Tensor<T>> for torsh_data::augmentation_pipeline::RandomVerticalFlip

type Output = Tensor<T>

impl<T: FloatElement> Transform<Tensor<T>> for torsh_data::tensor_transforms::RandomHorizontalFlip

type Output = Tensor<T>

impl<T: FloatElement> Transform<Tensor<T>> for torsh_data::tensor_transforms::Resize

type Output = Tensor<T>

impl<T: TensorElement> Transform<Tensor<T>> for torsh_data::tensor_transforms::CenterCrop

type Output = Tensor<T>

impl<T: TensorElement> Transform<Tensor<T>> for RandomCrop

type Output = Tensor<T>