Struct dfdx::nn::modules::AvgPoolGlobal

pub struct AvgPoolGlobal;

Applies average pooling over an entire image, fully reducing the height and width dimensions:

• Reduces 3d (C, H, W) to 1d (C, )
• Reduces 4d (B, C, H, W) to 2d (B, C)

Pytorch equivalent: torch.nn.AdaptiveAvgPool2d(1) followed by a flatten.

Examples:

let m: AvgPoolGlobal = Default::default();
let _: Tensor<Rank1<5>, f32, _> = m.forward(dev.zeros::<Rank3<5, 16, 8>>());
let _: Tensor<Rank2<10, 5>, f32, _> = m.forward(dev.zeros::<Rank4<10, 5, 16, 8>>());

Trait Implementations

impl Clone for AvgPoolGlobal

fn clone(&self) -> AvgPoolGlobal

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Default for AvgPoolGlobal

fn default() -> AvgPoolGlobal

Returns the “default value” for a type.

impl<B: Dim, C: Dim, H: Dim, W: Dim, E: Dtype, D: Device<E>, T: Tape<E, D>> Module<Tensor<(B, C, H, W), E, D, T>> for AvgPoolGlobal

type Output = Tensor<(B, C), E, D, T>

The type that this unit produces given Input.

type Error = <D as HasErr>::Err

fn try_forward( &self, input: Tensor<(B, C, H, W), E, D, T> ) -> Result<Self::Output, D::Err>

fn forward(&self, input: Input) -> Self::Output

Forward Input through the module and produce Module::Output.

impl<C: Dim, H: Dim, W: Dim, E: Dtype, D: Device<E>, T: Tape<E, D>> Module<Tensor<(C, H, W), E, D, T>> for AvgPoolGlobal

type Output = Tensor<(C,), E, D, T>

The type that this unit produces given Input.

type Error = <D as HasErr>::Err

fn try_forward( &self, input: Tensor<(C, H, W), E, D, T> ) -> Result<Self::Output, D::Err>

fn forward(&self, input: Input) -> Self::Output

Forward Input through the module and produce Module::Output.

impl Copy for AvgPoolGlobal

impl NonMutableModule for AvgPoolGlobal

impl ZeroSizedModule for AvgPoolGlobal