#![allow(clippy::bool_comparison)]
#![allow(clippy::unnecessary_cast)]
mod comparison;
mod ite;
pub use ite::IfThenElse;
pub use comparison::Comp;
use ndarray::*;
use crate::broadcast::multi_broadcast;
use crate::internal::*;
bin_to_super_type!(and, And,
[bool, u8, u16, u32, u64, i8, i16, i32, i64] => |c, &a, &b| *c = (a as i64 != 0 && b as i64 != 0) as _);
bin_to_super_type!(or, Or,
[bool, u8, u16, u32, u64, i8, i16, i32, i64] => |c, &a, &b| *c = (a as i64 != 0 || b as i64 != 0) as _);
bin_to_super_type!(xor, Xor, [bool] => |c, &a, &b| *c = a ^ b);
element_wise!(not, Not, [bool] => |_, vs| {
vs.iter_mut().for_each(|a| *a = !*a);
Ok(())
});
#[derive(Debug, Clone, new, Default, Hash)]
pub struct Iff;
impl Iff {
pub unsafe fn eval_t<T: Datum>(
cond: &ArrayViewD<bool>,
out: &mut Tensor,
t: &Tensor,
f: &Tensor,
) {
Zip::from(out.to_array_view_mut_unchecked::<T>())
.and_broadcast(cond)
.and_broadcast(t.to_array_view_unchecked::<T>())
.and_broadcast(f.to_array_view_unchecked::<T>())
.for_each(|r, c, t, f| *r = if *c { t.clone() } else { f.clone() })
}
}
impl Op for Iff {
fn name(&self) -> Cow<str> {
"Iff".into()
}
op_as_typed_op!();
}
impl EvalOp for Iff {
fn is_stateless(&self) -> bool {
true
}
fn eval(&self, inputs: TVec<TValue>) -> TractResult<TVec<TValue>> {
let (cond, t, f) = args_3!(inputs);
anyhow::ensure!(t.datum_type() == f.datum_type());
let shape: TVec<usize> = multi_broadcast(&[cond.shape(), t.shape(), f.shape()])?;
unsafe {
let mut result = Tensor::uninitialized_dt(t.datum_type(), &shape)?;
let cond = cond.to_array_view::<bool>()?;
dispatch_datum_by_size!(Self::eval_t(t.datum_type())(&cond, &mut result, &t, &f));
Ok(tvec!(result.into_tvalue()))
}
}
}
impl TypedOp for Iff {
as_op!();
fn output_facts(&self, inputs: &[&TypedFact]) -> TractResult<TVec<TypedFact>> {
anyhow::ensure!(inputs.len() == 3, "Iff expects 3 intputs.");
if inputs[1].datum_type != inputs[2].datum_type {
bail!("Then and else tensors type mismatch ({:?} and {:?}).", inputs[1], inputs[2]);
}
if inputs[0].rank() != inputs[1].rank() || inputs[0].rank() != inputs[2].rank() {
bail!("Inconsistent ranks, {:?}", inputs);
}
let shape = multi_broadcast(&[
inputs[0].shape.to_tvec(),
inputs[1].shape.to_tvec(),
inputs[2].shape.to_tvec(),
])
.unwrap();
Ok(tvec!(inputs[1].datum_type.fact(shape)))
}
fn axes_mapping(
&self,
inputs: &[&TypedFact],
outputs: &[&TypedFact],
) -> TractResult<AxesMapping> {
AxesMapping::natural(inputs, outputs)
}
}
bin_to_super_type!(bitand, BitAnd,
[bool, u8, u16, u32, u64, i8, i16, i32, i64] => |c, &a, &b| *c = a & b);
bin_to_super_type!(bitor, BitOr,
[bool, u8, u16, u32, u64, i8, i16, i32, i64] => |c, &a, &b| *c = a | b);
bin_to_super_type!(bitxor, BitXor,
[bool, u8, u16, u32, u64, i8, i16, i32, i64] => |c, &a, &b| *c = a ^ b);
element_wise!(bitnot, BitNot, [bool, u8, u16, u32, u64, i8, i16, i32, i64] => |_, xs| {
xs.iter_mut().for_each(|x| *x = !*x);
Ok(())
});