neuronika 0.2.0

Tensors and dynamic neural networks.
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#[cfg(test)]
use super::{assert_almost_equals, new_backward_input, new_input, new_tensor};
use super::{
    expect_tensor, expect_tensor_mut, push_gradient, Backward, Data, Forward, Gradient, Overwrite,
    Tensor,
};
use ndarray::Zip;
use std::{
    cell::{Cell, Ref, RefCell, RefMut},
    fmt::{Debug, Display},
    rc::Rc,
};

// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Transpose ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pub struct Transpose<T: Data> {
    operand: Rc<T>,
    data: RefCell<Tensor<T::Dim>>,
    computed: Cell<bool>,
}

impl<T: Data> Transpose<T> {
    pub fn new(operand: Rc<T>) -> Self {
        let data = Tensor::zeros(operand.data().t().raw_dim());

        Self {
            operand,
            data: RefCell::new(data),
            computed: Cell::new(false),
        }
    }
}

impl<T: Data> Forward for Transpose<T> {
    fn forward(&self) {
        if self.was_computed() {
            return;
        }

        self.computed.set(true);
        Zip::from(&mut *self.data.borrow_mut())
            .and(self.operand.data().t())
            .for_each(|v, o| *v = *o);
    }

    fn was_computed(&self) -> bool {
        self.computed.get()
    }

    fn reset_computation(&self) {
        self.computed.set(false);
    }
}

impl<T: Data> Data for Transpose<T> {
    type Dim = T::Dim;

    fn data(&self) -> Ref<Tensor<Self::Dim>> {
        self.data.borrow()
    }

    fn data_mut(&self) -> RefMut<Tensor<Self::Dim>> {
        self.data.borrow_mut()
    }
}

impl<T: Data> Debug for Transpose<T> {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("Transpose")
            .field("data", &self.data.borrow())
            .field("computed", &self.computed.get())
            .finish()
    }
}

impl<T: Data> Display for Transpose<T> {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> Result<(), std::fmt::Error> {
        write!(f, "{}", &self.data.borrow())
    }
}

// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ TransposeBackward ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pub struct TransposeBackward<T: Gradient + Overwrite> {
    gradient: RefCell<Option<Tensor<T::Dim>>>,
    shape: T::Dim,
    overwrite: Cell<bool>,
    operand: Rc<T>,
}

impl<T: Gradient + Overwrite> TransposeBackward<T> {
    pub fn new(operand: Rc<T>) -> Self {
        let shape = operand.gradient().t().raw_dim();

        Self {
            gradient: RefCell::new(Some(Tensor::zeros(shape.clone()))),
            shape,
            overwrite: Cell::new(true),
            operand,
        }
    }
}

impl<T: Gradient + Overwrite> Gradient for TransposeBackward<T> {
    type Dim = T::Dim;

    fn gradient(&self) -> Ref<Tensor<Self::Dim>> {
        expect_tensor(&self.gradient)
    }

    fn gradient_mut(&self) -> RefMut<Tensor<Self::Dim>> {
        expect_tensor_mut(&self.gradient)
    }
}

impl<T: Gradient + Overwrite> Overwrite for TransposeBackward<T> {
    fn can_overwrite(&self) -> bool {
        self.overwrite.get()
    }

    fn set_overwrite(&self, state: bool) {
        self.overwrite.set(state);
    }
}

impl<T: Gradient + Overwrite> Backward for TransposeBackward<T> {
    fn backward(&self) {
        push_gradient(&*self.operand, self.gradient().t());
    }

    fn no_grad(&self) {
        *self.gradient.borrow_mut() = None;
    }

    fn with_grad(&self) {
        *self.gradient.borrow_mut() = Some(Tensor::zeros(self.shape.clone()));
    }
}

impl<T> Debug for TransposeBackward<T>
where
    T: Gradient + Overwrite,
{
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("TransposeBackward")
            .field("gradient", &self.gradient.borrow())
            .field("overwrite", &self.overwrite.get())
            .finish()
    }
}

impl<T> Display for TransposeBackward<T>
where
    T: Gradient + Overwrite,
{
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> Result<(), std::fmt::Error> {
        match &*self.gradient.borrow() {
            Some(gradient) => write!(f, "{}", &gradient),
            None => write!(f, "None"),
        }
    }
}

// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Tests ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#[cfg(test)]
mod test;