use smallvec::smallvec;
use crate::{Element, Recordable, Shape, Tensor};
use super::{Cotangents, Operation, Reads, unary};
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub(crate) struct Pad {
pub(crate) axis: usize,
pub(crate) start: usize,
pub(crate) full_extent: usize,
}
impl Pad {
pub(crate) fn arity(&self) -> usize {
1
}
pub(crate) fn reads(&self) -> Reads {
Reads::NOTHING
}
pub(crate) fn infer_shape(&self, operands: &[Shape]) -> Shape {
let operand = unary(operands);
assert!(
self.axis < operand.rank(),
"pad axis {} is out of rank for {operand}",
self.axis
);
let len = operand.axes()[self.axis];
let end = self
.start
.checked_add(len)
.expect("pad window end overflows `usize`");
assert!(
end <= self.full_extent,
"pad window {}..{end} exceeds the full extent {}",
self.start,
self.full_extent
);
Shape::new(operand.axes().iter().enumerate().map(|(index, &extent)| {
if index == self.axis {
self.full_extent
} else {
extent
}
}))
}
}
impl Pad {
pub(crate) fn forward<E: Element>(&self, operands: &[&Tensor<E>]) -> Tensor<E> {
unary(operands).pad(self.axis, self.start, self.full_extent)
}
}
impl<Rule: Recordable> Operation<Rule> for Pad {
fn backward(&self, operands: &[&Rule], _output: &Rule, gradient: &Rule) -> Cotangents<Rule> {
let &operand = unary(operands);
let len = operand.shape().axes()[self.axis];
smallvec![Some(gradient.narrow(self.axis, self.start, len))]
}
}