dfdx 0.13.0

Ergonomic auto differentiation in Rust, with pytorch like apis.
Documentation 
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use crate::prelude::{Const, GenericUpscale2D, NearestNeighbor, UpscaleMethod};
use crate::prelude::{Dim, Dtype, HasErr, Tape, Tensor, Upscale2DKernel, ZerosTensor};

#[allow(unused)]
use super::{BuildModule, Module, NonMutableModule, ZeroSizedModule};

#[derive(Debug, Default, Clone)]
pub struct Upscale2D<const OH: usize, const OW: usize = OH, M: UpscaleMethod = NearestNeighbor>(M);

impl<const OH: usize, const OW: usize, M: UpscaleMethod> ZeroSizedModule for Upscale2D<OH, OW, M> {}
impl<const OH: usize, const OW: usize, M: UpscaleMethod> NonMutableModule for Upscale2D<OH, OW, M> {}

impl<const OH: usize, const OW: usize, M: UpscaleMethod, Img: GenericUpscale2D<M>> Module<Img>
    for Upscale2D<OH, OW, M>
{
    type Output = Img::Output<Const<OH>, Const<OW>>;
    type Error = Img::Err;

    fn try_forward(&self, x: Img) -> Result<Self::Output, Img::Err> {
        x.generic_upscale2d_like(M::default(), Const, Const)
    }
}

#[derive(Debug, Default, Clone)]
pub struct Upscale2DBy<const H: usize, const W: usize = H, M: UpscaleMethod = NearestNeighbor>(M);

impl<const H: usize, const W: usize, M: UpscaleMethod> ZeroSizedModule for Upscale2DBy<H, W, M> {}
impl<const H: usize, const W: usize, M: UpscaleMethod> NonMutableModule for Upscale2DBy<H, W, M> {}

#[cfg(feature = "nightly")]
impl<
        const H: usize,
        const W: usize,
        const IH: usize,
        const IW: usize,
        C: Dim,
        E: Dtype,
        M: UpscaleMethod,
        D: Upscale2DKernel<E, M> + ZerosTensor<E>,
        T: 'static + Tape<E, D>,
    > Module<Tensor<(C, Const<IH>, Const<IW>), E, D, T>> for Upscale2DBy<H, W, M>
where
    Tensor<(C, Const<{ IH * H }>, Const<{ IW * W }>), E, D, T>: Sized,
{
    type Output = Tensor<(C, Const<{ IH * H }>, Const<{ IW * W }>), E, D, T>;
    type Error = <Self::Output as HasErr>::Err;

    fn try_forward(
        &self,
        x: Tensor<(C, Const<IH>, Const<IW>), E, D, T>,
    ) -> Result<Self::Output, Self::Error> {
        x.generic_upscale2d_like(M::default(), Const, Const)
    }
}

#[cfg(feature = "nightly")]
impl<
        const H: usize,
        const W: usize,
        const IH: usize,
        const IW: usize,
        B: Dim,
        C: Dim,
        E: Dtype,
        M: UpscaleMethod,
        D: Upscale2DKernel<E, M> + ZerosTensor<E>,
        T: 'static + Tape<E, D>,
    > Module<Tensor<(B, C, Const<IH>, Const<IW>), E, D, T>> for Upscale2DBy<H, W, M>
where
    Tensor<(B, C, Const<{ IH * H }>, Const<{ IW * W }>), E, D, T>: Sized,
{
    type Output = Tensor<(B, C, Const<{ IH * H }>, Const<{ IW * W }>), E, D, T>;
    type Error = <Self::Output as HasErr>::Err;

    fn try_forward(
        &self,
        x: Tensor<(B, C, Const<IH>, Const<IW>), E, D, T>,
    ) -> Result<Self::Output, Self::Error> {
        x.generic_upscale2d_like(M::default(), Const, Const)
    }
}

impl<
        const H: usize,
        const W: usize,
        C: Dim,
        E: Dtype,
        M: UpscaleMethod,
        D: Upscale2DKernel<E, M> + ZerosTensor<E>,
        T: 'static + Tape<E, D>,
    > Module<Tensor<(C, usize, usize), E, D, T>> for Upscale2DBy<H, W, M>
{
    type Output = Tensor<(C, usize, usize), E, D, T>;
    type Error = <Self::Output as HasErr>::Err;

    fn try_forward(
        &self,
        x: Tensor<(C, usize, usize), E, D, T>,
    ) -> Result<Self::Output, Self::Error> {
        let shape = x.shape;
        x.generic_upscale2d_like(M::default(), shape.1 * H, shape.2 * W)
    }
}

impl<
        const H: usize,
        const W: usize,
        B: Dim,
        C: Dim,
        E: Dtype,
        M: UpscaleMethod,
        D: Upscale2DKernel<E, M> + ZerosTensor<E>,
        T: 'static + Tape<E, D>,
    > Module<Tensor<(B, C, usize, usize), E, D, T>> for Upscale2DBy<H, W, M>
where
    Tensor<(B, C, usize, usize), E, D, T>: Sized,
{
    type Output = Tensor<(B, C, usize, usize), E, D, T>;
    type Error = <Self::Output as HasErr>::Err;

    fn try_forward(
        &self,
        x: Tensor<(B, C, usize, usize), E, D, T>,
    ) -> Result<Self::Output, Self::Error> {
        let shape = x.shape;
        x.generic_upscale2d_like(M::default(), shape.2 * H, shape.3 * W)
    }
}
#[cfg(test)]
mod tests {
    use super::*;
    use crate::{shapes::*, tensor::*, tests::*};

    #[test]
    fn test_upscale2d() {
        let dev: TestDevice = Default::default();
        let x: Tensor<Rank3<3, 4, 4>, TestDtype, _> = dev.zeros();
        let _: Tensor<Rank3<3, 8, 8>, _, _> = Upscale2D::<8>::default().forward(x.clone());
        let _: Tensor<Rank3<3, 8, 12>, _, _> = Upscale2D::<8, 12>::default().forward(x.clone());
        let _: Tensor<Rank3<3, 9, 9>, _, _> =
            Upscale2D::<9, 9, NearestNeighbor>::default().forward(x);
    }

    #[cfg(feature = "nightly")]
    #[test]
    fn test_upscale2dby() {
        use crate::prelude::Bilinear;
        let dev: TestDevice = Default::default();
        let x: Tensor<Rank3<3, 4, 4>, TestDtype, _> = dev.zeros();
        let _: Tensor<Rank3<3, 8, 8>, _, _> = Upscale2DBy::<2>::default().forward(x.clone());
        let _: Tensor<Rank3<3, 8, 12>, _, _> = Upscale2DBy::<2, 3>::default().forward(x.clone());
        let _: Tensor<Rank3<3, 12, 12>, _, _> =
            Upscale2DBy::<3, 3, Bilinear>::default().forward(x.clone());
    }
}