skainet-data-transform/sk.ainet.data.transform/TransformScope

TransformScope

class TransformScope<T : DType, V>(val ctx: ExecutionContext)(source)

Scope that provides tensor transform DSL with an implicit execution context.

This allows for cleaner syntax when building pipelines within an execution context:

val pipeline = executionContext.transforms {
    pipeline<Tensor<FP32, Float>>()
        .rescale(255f)
        .normalize(ImageNet.mean, ImageNet.std)
}

Constructors

TransformScope

constructor(ctx: ExecutionContext)

Properties

ctx

val ctx: ExecutionContext

Functions

clamp

fun Transform<I, Tensor<T, V>>.clamp(min: Float, max: Float): Transform<I, Tensor<T, V>>

Chains a Clamp transform.

flatten

fun Transform<I, Tensor<T, V>>.flatten(startDim: Int = 0, endDim: Int = -1): Transform<I, Tensor<T, V>>

Chains a Flatten transform.

normalize

fun Transform<I, Tensor<T, V>>.normalize(mean: FloatArray, std: FloatArray, channelAxis: Int = -1): Transform<I, Tensor<T, V>>

Chains a Normalize transform.

rescale

fun Transform<I, Tensor<T, V>>.rescale(scale: Float = 255.0f): Transform<I, Tensor<T, V>>

Chains a Rescale transform.

reshape

fun Transform<I, Tensor<T, V>>.reshape(vararg dims: Int): Transform<I, Tensor<T, V>>

fun Transform<I, Tensor<T, V>>.reshape(shape: Shape): Transform<I, Tensor<T, V>>

Chains a Reshape transform.

scaleAndShift

fun Transform<I, Tensor<T, V>>.scaleAndShift(scale: Float, offset: Float = 0.0f): Transform<I, Tensor<T, V>>

Chains a ScaleAndShift transform.

squeeze

fun Transform<I, Tensor<T, V>>.squeeze(dim: Int? = null): Transform<I, Tensor<T, V>>

Chains a Squeeze transform.

unsqueeze

fun Transform<I, Tensor<T, V>>.unsqueeze(dim: Int): Transform<I, Tensor<T, V>>

Chains an Unsqueeze transform.