Trait collenchyma_nn::Pooling

pub trait Pooling<F>: NN<F> {
    fn new_pooling_config(
        &self, 
        window: &[i32], 
        padding: &[i32], 
        stride: &[i32]
    ) -> Result<Self::CPOOL, Error>;
    fn pooling_max(
        &self, 
        x: &mut SharedTensor<F>, 
        result: &mut SharedTensor<F>, 
        config: &Self::CPOOL
    ) -> Result<(), Error>;
    fn pooling_max_plain(
        &self, 
        x: &SharedTensor<F>, 
        result: &mut SharedTensor<F>, 
        config: &Self::CPOOL
    ) -> Result<(), Error>;
    fn pooling_max_grad(
        &self, 
        x: &mut SharedTensor<F>, 
        x_diff: &mut SharedTensor<F>, 
        result: &mut SharedTensor<F>, 
        result_diff: &mut SharedTensor<F>, 
        config: &Self::CPOOL
    ) -> Result<(), Error>;
    fn pooling_max_grad_plain(
        &self, 
        x: &SharedTensor<F>, 
        x_diff: &SharedTensor<F>, 
        result: &SharedTensor<F>, 
        result_diff: &mut SharedTensor<F>, 
        config: &Self::CPOOL
    ) -> Result<(), Error>;
}

Provides the functionality for a Backend to support Pooling operations.

Required Methods

fn new_pooling_config(
    &self,
    window: &[i32],
    padding: &[i32],
    stride: &[i32]
) -> Result<Self::CPOOL, Error>

Creates a new PoolingConfig, which needs to be passed to further pooling Operations.

fn pooling_max(
    &self,
    x: &mut SharedTensor<F>,
    result: &mut SharedTensor<F>,
    config: &Self::CPOOL
) -> Result<(), Error>

Computes non-linear down-sampling (max Pooling) over the input Tensor x with complete memory management.

Saves the result to result.

For a no-memory managed version see pooling_max_plain.

fn pooling_max_plain(
    &self,
    x: &SharedTensor<F>,
    result: &mut SharedTensor<F>,
    config: &Self::CPOOL
) -> Result<(), Error>

Computes the max pooling over the input Tensor x without any memory management.

Saves the result to result.

Attention:
For a correct computation result, you need to manage the memory allocation and synchronization yourself.
For a memory managed version see pooling_max.

fn pooling_max_grad(
    &self,
    x: &mut SharedTensor<F>,
    x_diff: &mut SharedTensor<F>,
    result: &mut SharedTensor<F>,
    result_diff: &mut SharedTensor<F>,
    config: &Self::CPOOL
) -> Result<(), Error>

Computes the gradient of max Pooling over the input Tensor x with complete memory management.

Saves the result to result_diff.

For a no-memory managed version see pooling_max_grad_plain.

fn pooling_max_grad_plain(
    &self,
    x: &SharedTensor<F>,
    x_diff: &SharedTensor<F>,
    result: &SharedTensor<F>,
    result_diff: &mut SharedTensor<F>,
    config: &Self::CPOOL
) -> Result<(), Error>

Computes the gradient of max pooling over the input Tensor x without any memory management.

Saves the result to result_diff.

Attention:
For a correct computation result, you need to manage the memory allocation and synchronization yourself.
For a memory managed version see pooling_max_grad.

Implementors

• impl Pooling<f32> for Backend<Cuda>
• impl Pooling<f64> for Backend<Cuda>