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//! Basic building blocks for neural networks.
//!
//! Neuronika provides some pre-assembled components, you can either use them individually or
//! combine them into a bigger architecture. Take a look at the [complete list](#layers) to know
//! more.
//!
//! You can also customize the initialization of the parameters of such components, and that of any
//! other differentiable variable, by picking the function that best fits your needs from the
//! [`nn::init`](module@init) module.
//!
//! Refer to the [`nn::loss`](module@loss) module for loss functions.
//!
//! # Assembling a neural network
//!
//! The suggested way of building a model using neuronika's building blocks is to define a struct
//! encapsulating its components.
//!
//! The behavior of the model should be defined by including an appropriate method in its struct
//! implementation. Such method must specify how the components interact.
//!
//! Consider, for the sake of simplicity, a classical *multilayer perceptron* with three dense
//! layers for a multivariate regression task, let's see what it would look like in neuronika.
//!
//! We begin by defining its struct using the provided components.
//!
//! ```
//! use neuronika::nn;
//!
//! // Network definition.
//! struct NeuralNetwork {
//! lin1: nn::Linear,
//! lin2: nn::Linear,
//! lin3: nn::Linear,
//! }
//! ```
//!
//! We'll also include a very simple constructor.
//!
//! ```
//! # use neuronika::nn;
//! # struct NeuralNetwork {
//! # lin1: nn::Linear,
//! # lin2: nn::Linear,
//! # lin3: nn::Linear,
//! # }
//! impl NeuralNetwork {
//! // Basic constructor.
//! fn new() -> Self {
//! Self {
//! lin1: nn::Linear::new(25, 30),
//! lin2: nn::Linear::new(30, 35),
//! lin3: nn::Linear::new(35, 5),
//! }
//! }
//! }
//! ```
//!
//! As the last step, we have to specify how the multilayer perceptron behaves, then, we're done.
//!
//! ```
//! use ndarray::Ix2;
//! use neuronika::{Backward, Data, Forward, Gradient, MatMatMulT, Overwrite, VarDiff};
//! use neuronika::nn::Learnable;
//!
//! # use neuronika::nn;
//! # struct NeuralNetwork {
//! # lin1: nn::Linear,
//! # lin2: nn::Linear,
//! # lin3: nn::Linear,
//! # }
//! impl NeuralNetwork {
//! // NeuralNetwork behavior. Notice the presence of the ReLU non-linearity.
//! fn forward<I, T, U>(
//! &self,
//! input: I,
//! ) -> VarDiff<
//! impl Data<Dim = Ix2> + Forward,
//! impl Gradient<Dim = Ix2> + Overwrite + Backward
//! >
//! where
//! I: MatMatMulT<Learnable<Ix2>>,
//! I::Output: Into<VarDiff<T, U>>,
//! T: Data<Dim = Ix2> + Forward,
//! U: Gradient<Dim = Ix2> + Backward + Overwrite,
//! {
//! let out1 = self.lin1.forward(input).relu();
//! let out2 = self.lin2.forward(out1).relu();
//! let out3 = self.lin3.forward(out2);
//! out3
//! }
//! }
//! ```
//!
//! Here's a fictitious example of the newly created multilayer perceptron in use.
//!
//! ```
//! # use neuronika::nn;
//! # use ndarray::Ix2;
//! # use neuronika::{Backward, Data, Forward, Gradient, MatMatMulT, Overwrite, VarDiff};
//! # use neuronika::nn::Learnable;
//! # #[cfg(feature = "blas")]
//! # extern crate blas_src;
//! # struct NeuralNetwork {
//! # lin1: nn::Linear,
//! # lin2: nn::Linear,
//! # lin3: nn::Linear,
//! # }
//! # impl NeuralNetwork {
//! # // Basic constructor.
//! # fn new() -> Self {
//! # Self {
//! # lin1: nn::Linear::new(25, 30),
//! # lin2: nn::Linear::new(30, 35),
//! # lin3: nn::Linear::new(35, 5),
//! # }
//! # }
//! # }
//! # impl NeuralNetwork {
//! # // NeuralNetwork behavior. Notice the presence of the ReLU non-linearity.
//! # fn forward<I, T, U>(
//! # &self,
//! # input: I,
//! # ) -> VarDiff<
//! # impl Data<Dim = Ix2> + Forward,
//! # impl Gradient<Dim = Ix2> + Overwrite + Backward
//! # >
//! # where
//! # I: MatMatMulT<Learnable<Ix2>>,
//! # I::Output: Into<VarDiff<T, U>>,
//! # T: Data<Dim = Ix2> + Forward,
//! # U: Gradient<Dim = Ix2> + Backward + Overwrite,
//! # {
//! # let out1 = self.lin1.forward(input).relu();
//! # let out2 = self.lin2.forward(out1).relu();
//! # let out3 = self.lin3.forward(out2);
//! # out3
//! # }
//! # }
//! let model = NeuralNetwork::new();
//!
//! // Random data to be given in input to the model.
//! let fictitious_data = neuronika::rand((200, 25));
//!
//! let out = model.forward(fictitious_data);
//! out.forward(); // Always remember to call forward() !
//! # assert_eq!(out.data().shape(), &[200, 5]);
//! ```
//! # Tracking parameters with ModelStatus
//!
//! In some circumstances you may find useful to group the parameters of a model. Consider for
//! instance the following scenario.
//!
//! ```
//! # use neuronika::nn;
//! # use ndarray::Ix2;
//! # use neuronika::{Backward, Data, Forward, Gradient, MatMatMulT, Overwrite, VarDiff};
//! # use neuronika::nn::Learnable;
//! # #[cfg(feature = "blas")]
//! # extern crate blas_src;
//! # struct NeuralNetwork {
//! # lin1: nn::Linear,
//! # lin2: nn::Linear,
//! # lin3: nn::Linear,
//! # }
//! # impl NeuralNetwork {
//! # // Basic constructor.
//! # fn new() -> Self {
//! # Self {
//! # lin1: nn::Linear::new(25, 30),
//! # lin2: nn::Linear::new(30, 35),
//! # lin3: nn::Linear::new(35, 5),
//! # }
//! # }
//! # }
//! # impl NeuralNetwork {
//! # // NeuralNetwork behavior. Notice the presence of the ReLU non-linearity.
//! # fn forward<I, T, U>(
//! # &self,
//! # input: I,
//! # ) -> VarDiff<
//! # impl Data<Dim = Ix2> + Forward,
//! # impl Gradient<Dim = Ix2> + Overwrite + Backward
//! # >
//! # where
//! # I: MatMatMulT<Learnable<Ix2>>,
//! # I::Output: Into<VarDiff<T, U>>,
//! # T: Data<Dim = Ix2> + Forward,
//! # U: Gradient<Dim = Ix2> + Backward + Overwrite,
//! # {
//! # let out1 = self.lin1.forward(input).relu();
//! # let out2 = self.lin2.forward(out1).relu();
//! # let out3 = self.lin3.forward(out2);
//! # out3
//! # }
//! # }
//! let model = NeuralNetwork::new();
//!
//! let some_other_variable = neuronika::rand((1, 25)).requires_grad();
//!
//! // Random perturbed data.
//! let fictitious_data = neuronika::rand((200, 25)) + some_other_variable;
//!
//! let out = model.forward(fictitious_data);
//! assert_eq!(out.parameters().len(), 7); // 7 leaf ancestors !
//! ```
//!
//! You may notice how, if we feed in input to our neural network the result of an addition
//! operation, in which one of the operands is a differentiable variable, and then
//! request the network output's differentiable ancestors, we are given a vector containing 7
//! [`Param`](struct@Param).
//!
//! By doing some quick math: 7 = 2 * 3 + 1, and by noticing that each of the three linear layers
//! that the multilayer perceptron is made of has one learnable weight matrix and one learnable
//! bias vector, we can conclude that the presence of the seventh ancestors is due to the addition
//! between `fictitious_data` and `some_other_variable`.
//!
//! In fact, neuronika automatically tracks all the differentiable leaves that are involved in the
//! computation of the output variable when assembling the computational graph corresponding to
//! the issued operations.
//!
//! If you need to distinguish between the parameters of a model and another differentiable variable
//! or between the parameters of several different models, you can use [`ModelStatus`].
//!
//! With `ModelStatus` you can build the exact same neural network only varying the implementation
//! so slightly.
//!
//! ```
//! use neuronika::Param;
//! use neuronika::nn::{ModelStatus, Linear};
//!
//! struct NeuralNetwork {
//! lin1: Linear,
//! lin2: Linear,
//! lin3: Linear,
//! status: ModelStatus,
//! }
//!
//! impl NeuralNetwork {
//! fn new() -> Self {
//! // Initialize an empty model status.
//! let mut status = ModelStatus::default();
//!
//! // We register each component whilst at the same time building the network.
//! Self {
//! lin1: status.register(Linear::new(25, 30)),
//! lin2: status.register(Linear::new(30, 35)),
//! lin3: status.register(Linear::new(35, 5)),
//! status,
//! }
//! }
//!
//! /// Returns the model's parameters.
//! fn parameters(&self) -> Vec<Param> {
//! // We are now able to access the parameter of the neural network.
//! self.status.parameters()
//! }
//! }
//! ```
//!
//! At last, we verify that the number of registered parameters for the new version of our neural
//! network is indeed 6.
//!
//! ```
//! # use neuronika::Param;
//! # use neuronika::nn::{ModelStatus, Linear, Learnable};
//! # struct NeuralNetwork {
//! # lin1: Linear,
//! # lin2: Linear,
//! # lin3: Linear,
//! # status: ModelStatus,
//! # }
//! # impl NeuralNetwork {
//! # // Basic constructor.
//! # fn new() -> Self {
//! # let mut status = ModelStatus::default();
//! #
//! # Self {
//! # lin1: status.register(Linear::new(25, 30)),
//! # lin2: status.register(Linear::new(30, 35)),
//! # lin3: status.register(Linear::new(35, 5)),
//! # status,
//! # }
//! # }
//! #
//! # fn parameters(&self) -> Vec<Param> {
//! # self.status.parameters()
//! # }
//! # }
//! let model = NeuralNetwork::new();
//! assert_eq!(model.parameters().len(), 6);
//! ```
//! Do also note that in spite of the introduction of `ModelStatus`, the implementation of the
//! `.forward()` method has not changed at all.
//!
//! # Train and Eval
//!
//! The status of a model determines the behavior of its components. Certain building blocks, such
//! as the [`Dropout`], are turned on and off depending on whether the model is running in *training
//! mode* or in *inference mode*.
//!
//! You can set a network in training mode or in inference mode either by calling [`.train()`] and
//! [`.eval()`] directly on its output or by using `ModelStatus`.
//!
//! The former approach is preferable, as when multiple models are pipelined, calling `.train()`
//! and `.eval()` directly on the final outputs will switch the statuses of all the models.
//! Do also note that switching the status by using `ModelStatus` is the only way that allows for
//! selectively training and evaluating multiple models.
//!
//! Let's picture it with a simple example.
//!
//! [`.eval()`]: VarDiff::eval()
//! [`.train()`]: VarDiff::train()
//!
//! ```
//! use neuronika::Param;
//! use neuronika::nn::{ModelStatus, Linear, Dropout};
//!
//! struct NeuralNetwork {
//! lin1: Linear,
//! drop: Dropout,
//! lin2: Linear,
//! status: ModelStatus,
//! }
//!
//! impl NeuralNetwork {
//! fn new() -> Self {
//! let mut status = ModelStatus::default();
//!
//! // Similarly to what we did before, we register the components
//! // to the network's status.
//! // Now the dropout layer, and every other changeable
//! // component, can be directly controlled by interacting
//! // with the model itself, as it is synced with the one of
//! // ModelStatus.
//! Self {
//! lin1: status.register(Linear::new(25, 35)),
//! drop: status.register(Dropout::new(0.5)),
//! lin2: status.register(Linear::new(35, 5)),
//! status,
//! }
//! }
//!
//! fn parameters(&self) -> Vec<Param> {
//! self.status.parameters()
//! }
//!
//! /// Switches the network in training mode.
//! fn train(&self) {
//! self.status.train()
//! }
//!
//! /// Switches the network in inference mode.
//! fn eval(&self) {
//! self.status.eval()
//! }
//! }
//! ```
//!
//! # Layers
//!
//! Here are listed all neuronika's building blocks.
//!
//! ## Linear Layers
//!
//! * [`nn::Linear`](struct@Linear) - Applies a linear transformation to the incoming data.
//!
//! ## Recurrent Layers
//!
//! * [`nn::GRUCell`](struct@GRUCell) - A gated recurrent unit cell.
//!
//! * [`nn::LSTMCell`](struct@LSTMCell) - A long short term memory cell.
//!
//! ## Convolution Layers
//!
//! * [`nn::Conv1d`](struct@Conv1d) - Applies a temporal convolution over an input signal composed
//! of several input planes.
//!
//! * [`nn::GroupedConv1d`](struct@GroupedConv1d) - Applies a grouped temporal convolution over an
//! input signal composed of several input planes.
//!
//! * [`nn::Conv2d`](struct@Conv2d) - Applies a spatial convolution over an input signal composed
//! of several input planes.
//!
//! * [`nn::GroupedConv2d`](struct@GroupedConv2d) - Applies a grouped spatial convolution over an
//! input signal composed of several input planes.
//!
//! * [`nn::Conv3d`](struct@Conv3d) - Applies a volumetric convolution over an input signal composed
//! of several input planes.
//!
//! * [`nn::GroupedConv3d`](struct@GroupedConv3d) - Applies a grouped volumetric convolution over an
//! input signal composed of several input planes.
//!
//! ## Dropout Layers
//!
//! * [`nn::Dropout`](struct@Dropout) - During training, randomly zeroes some of the elements of
//! the input variable with probability *p* using samples from a Bernoulli distribution.
use super::{Input, InputBackward, Param};
use crate::variable::{
self, Backward, Convolve, ConvolveWithGroups, Data, Dropout as DropoutNode,
DropoutBackward as DropoutBackwardNode, Eval, Forward, Gradient, MatMatMulT, Overwrite,
RawParam, Tensor, Var, VarDiff,
};
pub use crate::variable::{Constant, PaddingMode, Reflective, Replicative, Zero};
use ndarray::{Ix1, Ix2, Ix3, Ix4, Ix5};
use std::{cell::Cell, rc::Rc};
pub mod init;
pub mod loss;
#[cfg(feature = "serialize")]
use serde::{Deserialize, Serialize};
/// A generic parameter of a neural component.
pub type Learnable<D> = VarDiff<Input<D>, InputBackward<D>>;
/// A model's components status.
///
/// This struct should be used when you are interested in keeping track of the statuses and the
/// parameters of the components that are part of a neural network. There are many circumstances in
/// which this can be useful, such as when you have more than one model in a pipeline.
///
/// This struct stores all the [`Learnable`] associated to a given model and the model's status. It
/// is suggested to perform the registration of the layers at the model construction.
pub struct ModelStatus {
params: Vec<RawParam>,
train: Rc<Cell<bool>>,
}
impl ModelStatus {
/// Returns a vector of [`Param`] linked to the learnable weights associated to a neural
/// network.
///
/// Conceptually, this method behaves similarly to [`.parameters()`](VarDiff::parameters()) when
/// called on the differentiable variable outputted by the network. The key difference lies in
/// that, while the differentiable variable's `.parameters()` method would return *all*
/// the differentiable leaves that took part in the computation of the output, possibly also
/// the weights of another network, `ModelStatus`'s `.parameters()` method returns *only* the
/// leaves that have been associated with it at the model's instantiation.
///
/// Usually the result of this method is passed to an optimizer.
pub fn parameters(&self) -> Vec<Param<'_>> {
self.params
.iter()
.cloned()
.map(RawParam::into_param)
.collect()
}
/// Registers a component.
///
/// # Arguments
///
/// `component` - layer to be registered.
pub fn register<T: Register>(&mut self, mut component: T) -> T {
component.register_params(&mut self.params);
component.register_status(self.train.clone());
component
}
/// Sets the status in training mode.
pub fn train(&self) {
<Self as Eval>::train(self)
}
/// Sets the status in inference mode.
pub fn eval(&self) {
<Self as Eval>::eval(self)
}
}
impl Default for ModelStatus {
/// Returns a new `ModelStatus` with empty parameters and status set to train.
fn default() -> Self {
Self {
params: Vec::new(),
train: Rc::new(Cell::new(true)),
}
}
}
impl Eval for ModelStatus {
/// Sets the status to train.
fn train(&self) {
self.train.set(true)
}
/// Sets the status to eval.
fn eval(&self) {
self.train.set(false)
}
}
/// Dropout input.
///
/// This trait is implemented by `Var` and `VarDiff`.
pub trait DropoutInput {
type Output;
fn dropout(self, p: f64, status: Rc<Cell<bool>>) -> Self::Output;
}
impl<T, U> DropoutInput for VarDiff<T, U>
where
T: Data + Forward,
U: Gradient<Dim = T::Dim> + Overwrite + Backward,
{
type Output = VarDiff<DropoutNode<T>, DropoutBackwardNode<U, T>>;
fn dropout(self, p: f64, status: Rc<Cell<bool>>) -> Self::Output {
self.dropout_with_status(p, status)
}
}
impl<T> DropoutInput for Var<T>
where
T: Data + Forward,
{
type Output = Var<DropoutNode<T>>;
fn dropout(self, p: f64, status: Rc<Cell<bool>>) -> Self::Output {
self.dropout_with_status(p, status)
}
}
/// Registration for neuronika's components.
pub trait Register {
/// Registers `self`'s parameters to the model's status parameters `params`.
fn register_params(&self, params: &mut Vec<RawParam>);
/// Register `self`'s status to the model's status state `status`.
fn register_status(&mut self, status: Rc<Cell<bool>>);
}
/// During training, randomly zeroes some of the elements of `self` with probability *p* using
/// samples from a Bernoulli distribution. Each channel will be zeroed out independently on
/// every forward call.
///
/// This has proven to be an effective technique for regularization and preventing the
/// co-adaptation of neurons as described in the paper
/// [Improving neural networks by preventing co-adaptation of feature detectors](https://arxiv.org/abs/1207.0580).
///
/// Furthermore, the outputs are scaled by a factor of 1/(1 - p) during training. This means
/// that during evaluation the resulting variable simply computes an identity function.
pub struct Dropout {
pub status: Rc<Cell<bool>>,
pub p: f64,
}
impl Dropout {
/// Creates a dropout layer.
///
/// # Arguments
///
/// `p` - probability of an element to be zeroed.
pub fn new(p: f64) -> Self {
let status = Rc::new(Cell::new(true));
Self { status, p }
}
/// Applies the dropout to the variable in input.
///
/// # Arguments
///
/// `input` - variable in input to the layer.
pub fn forward<I: DropoutInput>(&self, input: I) -> I::Output {
input.dropout(self.p, self.status.clone())
}
}
impl Eval for Dropout {
fn eval(&self) {
self.status.set(false)
}
fn train(&self) {
self.status.set(true)
}
}
impl Register for Dropout {
fn register_status(&mut self, status: Rc<Cell<bool>>) {
self.status = status;
}
fn register_params(&self, _: &mut Vec<RawParam>) {}
}
/// Applies a **linear transformation** to the incoming data.
///
/// ```text
/// ʏ = xAᵀ + b
/// ```
#[cfg_attr(feature = "serialize", derive(Serialize, Deserialize))]
pub struct Linear {
pub weight: Learnable<Ix2>,
pub bias: Learnable<Ix1>,
}
impl Linear {
/// Creates a linear layer.
///
/// # Arguments
///
/// * `in_features` – size of each input sample.
///
/// * `out_features` – size of each output sample.
///
/// The learnable weight of the layer is of shape `(out_features, in_features)`. The learnable
/// bias of the layer is of shape `out_features`.
///
/// The values for both the weight and bias are initialized from *U(-k, k)* where
/// `k = (1. / in_features as f32).sqrt()`.
pub fn new(in_features: usize, out_features: usize) -> Self {
let weight = Input::new(Tensor::zeros((out_features, in_features))).requires_grad();
let bias = Input::new(Tensor::zeros(out_features)).requires_grad();
let k = (1. / (in_features as f32)).sqrt();
init::uniform(&weight, -k, k);
init::uniform(&bias, -k, k);
Self { weight, bias }
}
/// Applies the linear transformation *y = xA^T + b* to the incoming data.
///
/// # Arguments
///
/// `data` - a variable of shape *(N, in_features)*, the output's shape will be
/// *(N, out_features)*.
pub fn forward<I, T, U>(
&self,
input: I,
) -> VarDiff<impl Data<Dim = Ix2> + Forward, impl Gradient<Dim = Ix2> + Overwrite + Backward>
where
I: MatMatMulT<Learnable<Ix2>>,
I::Output: Into<VarDiff<T, U>>,
T: Data<Dim = Ix2>,
U: Gradient<Dim = Ix2> + Overwrite,
{
input.mm_t(self.weight.clone()).into() + self.bias.clone()
}
}
impl Register for Linear {
/// Registers the weight and the bias of this `Linear` instance.
fn register_params(&self, params: &mut Vec<RawParam>) {
self.weight.register_params(params);
self.bias.register_params(params);
}
fn register_status(&mut self, _: Rc<Cell<bool>>) {}
}
/// A **long short-term memory (LSTM)** cell.
#[cfg_attr(feature = "serialize", derive(Serialize, Deserialize))]
#[allow(clippy::upper_case_acronyms)]
pub struct LSTMCell {
pub weight_ih: Learnable<Ix2>,
pub weight_hh: Learnable<Ix2>,
pub bias_ih: Learnable<Ix1>,
pub bias_hh: Learnable<Ix1>,
}
impl LSTMCell {
/// Creates a new LSTMCell.
///
/// # Arguments
///
/// * `input_size` - number of expected features in the input.
///
/// * `hidden_size` - number of features in the hidden state.
///
/// All the weight and biases are initialized from *U(-k, k)* where
/// `k = (1. / hidden_size as f32).sqrt()`.
pub fn new(input_size: usize, hidden_size: usize) -> Self {
let (weight_ih_shape, weight_hh_shape, bias_shape) = {
let xhidden_size = 4 * hidden_size;
(
(xhidden_size, input_size),
(xhidden_size, hidden_size),
xhidden_size,
)
};
let weight_ih = Input::new(Tensor::zeros(weight_ih_shape)).requires_grad();
let weight_hh = Input::new(Tensor::zeros(weight_hh_shape)).requires_grad();
let bias_ih = Input::new(Tensor::zeros(bias_shape)).requires_grad();
let bias_hh = Input::new(Tensor::zeros(bias_shape)).requires_grad();
let k = 1. / (hidden_size as f32).sqrt();
init::uniform(&weight_ih, -k, k);
init::uniform(&weight_hh, -k, k);
init::uniform(&bias_ih, -k, k);
init::uniform(&bias_hh, -k, k);
Self {
weight_ih,
weight_hh,
bias_ih,
bias_hh,
}
}
/// Computes a single **LSTM step**.
///
/// # Arguments
///
/// * `state` - a tuple of tensors, both of shape *(batch, hidden_size)*, containing the
/// initial hidden state for each element in the batch and the initial cell's state for
/// each element in the batch.
///
/// * `input` - a variable containing the input features of shape *(batch, input_size)*.
///
/// The **output** is a tuple of tensors made of the next hidden state for each element in
/// the batch, of shape *(batch, hidden_size)* and the next cell's state for each element in
/// the batch, of shape *(batch, hidden_size)*.
pub fn forward<Cf, Cb, Hf, Hb, I, T, U>(
&self,
state: (VarDiff<Cf, Cb>, VarDiff<Hf, Hb>),
input: I,
) -> (
VarDiff<impl Data<Dim = Ix2> + Forward, impl Gradient<Dim = Ix2> + Overwrite + Backward>,
VarDiff<impl Data<Dim = Ix2> + Forward, impl Gradient<Dim = Ix2> + Overwrite + Backward>,
)
where
Cf: Data<Dim = Ix2>,
Cb: Gradient<Dim = Ix2> + Overwrite,
Hf: Data<Dim = Ix2>,
Hb: Gradient<Dim = Ix2> + Overwrite,
I: MatMatMulT<Learnable<Ix2>>,
I::Output: Into<VarDiff<T, U>>,
T: Data<Dim = Ix2>,
U: Gradient<Dim = Ix2> + Overwrite,
{
let (cell_state, hidden) = state;
let gates = hidden.mm_t(self.weight_hh.clone())
+ self.bias_hh.clone()
+ input.mm_t(self.weight_ih.clone()).into()
+ self.bias_ih.clone();
let gate_shape = {
let (gates_shape_rows, gates_shape_cols) = gates.data().dim();
(gates_shape_rows, gates_shape_cols / 4)
};
let chunked_gates = gates.chunks(gate_shape);
let (input_gate, forget_gate, cell_state_gate, output_gate) = (
chunked_gates[0].clone().sigmoid(),
chunked_gates[1].clone().tanh(),
chunked_gates[2].clone().sigmoid(),
chunked_gates[3].clone().sigmoid(),
);
let new_cell_state = forget_gate * cell_state + (input_gate * cell_state_gate);
let new_hidden = output_gate * new_cell_state.clone().tanh();
(new_cell_state, new_hidden)
}
}
impl Register for LSTMCell {
/// Registers the weights and the biases of this LSTMCell instance.
fn register_params(&self, params: &mut Vec<RawParam>) {
self.weight_hh.register_params(params);
self.weight_ih.register_params(params);
self.bias_hh.register_params(params);
self.bias_ih.register_params(params);
}
fn register_status(&mut self, _: Rc<Cell<bool>>) {}
}
/// A **gated recurrent unit (GRU)** cell.
#[cfg_attr(feature = "serialize", derive(Serialize, Deserialize))]
#[allow(clippy::upper_case_acronyms)]
pub struct GRUCell {
pub weight_ih: Learnable<Ix2>,
pub weight_hh: Learnable<Ix2>,
pub bias_ih: Learnable<Ix1>,
pub bias_hh: Learnable<Ix1>,
}
impl GRUCell {
/// Creates a new GRUCell.
///
/// # Arguments
///
/// * `input_size` - number of expected features in the input.
///
/// * `hidden_size` - number of features in the hidden state.
///
/// All the weight and biases are initialized from *U(-k, k)* where
/// `k = (1. / hidden_size as f32).sqrt()`.
pub fn new(input_size: usize, hidden_size: usize) -> Self {
let (weight_ih_shape, weight_hh_shape, bias_shape) = {
let xhidden_size = 3 * hidden_size;
(
(xhidden_size, input_size),
(xhidden_size, hidden_size),
xhidden_size,
)
};
let weight_ih = Input::new(Tensor::zeros(weight_ih_shape)).requires_grad();
let weight_hh = Input::new(Tensor::zeros(weight_hh_shape)).requires_grad();
let bias_ih = Input::new(Tensor::zeros(bias_shape)).requires_grad();
let bias_hh = Input::new(Tensor::zeros(bias_shape)).requires_grad();
let k = 1. / (hidden_size as f32).sqrt();
init::uniform(&weight_ih, -k, k);
init::uniform(&weight_hh, -k, k);
init::uniform(&bias_ih, -k, k);
init::uniform(&bias_hh, -k, k);
Self {
weight_ih,
weight_hh,
bias_ih,
bias_hh,
}
}
/// Computes a single **GRU step**.
///
/// * `hidden` - a variable of shape *(batch, hidden_size)*, containing the initial hidden state
/// for each element in the batch.
///
/// * `input` - a variable containing the input features of shape *(batch, input_size)*.
///
/// The **output** is a variable made of the next hidden state for each element in
/// the batch, of shape *(batch, hidden_size)*.
pub fn forward<Hf, Hb, I, T, U>(
&self,
hidden: VarDiff<Hf, Hb>,
input: I,
) -> VarDiff<impl Data<Dim = Ix2> + Forward, impl Gradient<Dim = Ix2> + Overwrite + Backward>
where
Hf: Data<Dim = Ix2>,
Hb: Gradient<Dim = Ix2> + Overwrite,
I: MatMatMulT<Learnable<Ix2>>,
I::Output: Into<VarDiff<T, U>>,
T: Data<Dim = Ix2>,
U: Gradient<Dim = Ix2> + Overwrite,
{
let (igates, hgates) = {
(
input.mm_t(self.weight_ih.clone()).into() + self.bias_ih.clone(),
hidden.clone().mm_t(self.weight_hh.clone()) + self.bias_hh.clone(),
)
};
let gate_shape = {
let (gates_shape_rows, gates_shape_cols) = hgates.data().dim();
(gates_shape_rows, gates_shape_cols / 3)
};
let (chunked_igates, chunked_hgates) =
(igates.chunks(gate_shape), hgates.chunks(gate_shape));
let reset_gate = (chunked_hgates[0].clone() + chunked_igates[0].clone()).sigmoid();
let input_gate = (chunked_hgates[1].clone() + chunked_igates[1].clone()).sigmoid();
let new_gate =
(chunked_igates[2].clone() + (chunked_hgates[2].clone() * reset_gate)).tanh();
(hidden - new_gate.clone()) * input_gate + new_gate
}
}
impl Register for GRUCell {
/// Registers the weights and the biases of this `GRUCell` instance.
fn register_params(&self, params: &mut Vec<RawParam>) {
self.weight_hh.register_params(params);
self.weight_ih.register_params(params);
self.bias_hh.register_params(params);
self.bias_ih.register_params(params);
}
fn register_status(&mut self, _: Rc<Cell<bool>>) {}
}
/// Applies a **temporal convolution** over an input signal composed of several input planes.
///
/// See also [`GroupedConv1d`].
#[cfg_attr(feature = "serialize", derive(Serialize, Deserialize))]
pub struct Conv1d<Pad: PaddingMode> {
pub padding: usize,
pub padding_mode: Pad,
pub stride: usize,
pub dilation: usize,
pub weight: Learnable<Ix3>,
pub bias: Learnable<Ix1>,
}
impl<Pad: PaddingMode> Conv1d<Pad> {
/// Creates a new Conv1d.
///
/// # Arguments
///
/// * `in_channels` - number of planes in the input signal.
///
/// * `out_channels` - number of planes in the output signal.
///
/// * `kernel_size` - size of the kernel, a number for this one-dimensional case.
///
/// * `padding` - padding to be applied to the input, a number for this one-dimensional case.
///
/// * `padding_mode` - padding mode, it can be: [`Zero`], [`Constant`], [`Reflective`] or
/// [`Replicative`].
///
/// * `stride` - stride of the convolution, a number for this one-dimensional case.
///
/// * `dilation` - controls the spacing between the kernel points, a number for this
/// one-dimensional case.
///
/// The weight and the bias of the layer are initialized from *U(-k, k)* where
/// `k = (1. /(in_channels * kernel_size) as f32).sqrt()`.
pub fn new(
in_channels: usize,
out_channels: usize,
kernel_size: usize,
padding: usize,
padding_mode: Pad,
stride: usize,
dilation: usize,
) -> Self {
let weight =
Input::new(Tensor::zeros((out_channels, in_channels, kernel_size))).requires_grad();
let bias = Input::new(Tensor::zeros(out_channels)).requires_grad();
let k = (1. / (in_channels * kernel_size) as f32).sqrt();
init::uniform(&weight, -k, k);
init::uniform(&bias, -k, k);
Self {
padding,
padding_mode,
stride,
dilation,
weight,
bias,
}
}
/// Computes a 1-dimensional convolution *(cross correlation)*.
///
/// # Arguments
///
/// `input` - signal to convolve.
///
/// The **input** must be of shape *(N, Cin, L)*
/// * **N** is the batch size
/// * **Cin** is the number of input channels
/// * **L** is the **length** of the input
///
/// The **kernel** must be of shape *(Cout, Cin, Lk)*
/// * **Cout** is the number of output channels
/// * **Cin** is the number of input channels
/// * **Lk** is the **length** of the kernel
///
/// The resulting output shape will be *(N, Cout, Lout)*
pub fn forward<I, T, U>(
&self,
input: I,
) -> VarDiff<impl Data<Dim = Ix3> + Forward, impl Gradient<Dim = Ix3> + Overwrite + Backward>
where
I: Convolve<I, Learnable<Ix3>, Pad>,
I::Output: Into<VarDiff<T, U>>,
T: Data<Dim = Ix3>,
U: Gradient<Dim = Ix3> + Overwrite,
{
I::convolve(
input,
self.weight.clone(),
&[self.stride],
&[self.dilation],
&[self.padding],
self.padding_mode.clone(),
)
.into()
+ self.bias.clone()
}
}
impl<Pad: PaddingMode> Register for Conv1d<Pad> {
/// Registers the weight and the bias of this `Conv1d` instance.
fn register_params(&self, params: &mut Vec<RawParam>) {
self.weight.register_params(params);
self.bias.register_params(params);
}
fn register_status(&mut self, _: Rc<Cell<bool>>) {}
}
/// Applies a **grouped temporal convolution** over an input signal composed of several input
/// planes.
#[cfg_attr(feature = "serialize", derive(Serialize, Deserialize))]
pub struct GroupedConv1d<Pad: PaddingMode> {
pub padding: usize,
pub padding_mode: Pad,
pub stride: usize,
pub dilation: usize,
pub groups: usize,
pub weight: Learnable<Ix3>,
pub bias: Learnable<Ix1>,
}
impl<Pad: PaddingMode> GroupedConv1d<Pad> {
/// Creates a new GroupedConv1d.
///
/// # Arguments
///
/// * `in_channels` - number of planes in the input signal.
///
/// * `out_channels` - number of planes in the output signal.
///
/// * `kernel_size` - size of the kernel, a number for this one-dimensional case.
///
/// * `padding` - padding to be applied to the input, a number for this one-dimensional case.
///
/// * `padding_mode` - padding mode, it can be: [`Zero`], [`Constant`], [`Reflective`] or
/// [`Replicative`].
///
/// * `stride` - stride of the convolution, a number for this one-dimensional case.
///
/// * `dilation` - controls the spacing between the kernel points, a number for this
/// one-dimensional case.
///
/// * `groups` - controls the connections between inputs and outputs. `in_channels` and
/// `out_channels` must both be **divisible by groups**.
///
/// For example:
/// * at `groups = 1`, all inputs are convolved to all outputs.
/// * at `groups = 2`, the operation becomes equivalent to having two convolutional layers side
/// by side, each seeing half the input channels and producing half the output channels, and
/// both subsequently concatenated.
///* at `groups = in_channels`, each input channel is convolved with its own set of filters.
///
/// The weight and the bias of the layer are initialized from *U(-k, k)* where
/// `k = (groups /(in_channels * kernel_size) as f32).sqrt()`.
#[allow(clippy::too_many_arguments)]
pub fn new(
in_channels: usize,
out_channels: usize,
kernel_size: usize,
padding: usize,
padding_mode: Pad,
stride: usize,
dilation: usize,
groups: usize,
) -> Self {
let weight = Input::new(Tensor::zeros((
out_channels,
in_channels / groups,
kernel_size,
)))
.requires_grad();
let bias = Input::new(Tensor::zeros(out_channels)).requires_grad();
let k = (groups as f32 / (in_channels * kernel_size) as f32).sqrt();
init::uniform(&weight, -k, k);
init::uniform(&bias, -k, k);
Self {
padding,
padding_mode,
stride,
dilation,
groups,
weight,
bias,
}
}
/// Computes a 1-dimensional grouped convolution *(cross correlation)*.
///
/// # Arguments
///
/// `input` - signal to convolve.
///
/// The **input** must be of shape *(N, Cin, L)*
/// * **N** is the batch size
/// * **Cin** is the number of input channels
/// * **L** is the **length** of the input
///
/// The **kernel** must be of shape *(Cout, Cin, Lk)*
/// * **Cout** is the number of output channels
/// * **Cin** is the number of input channels
/// * **Lk** is the **length** of the kernel
///
/// The resulting output shape will be *(N, Cout, Lout)*
pub fn forward<I, T, U>(
&self,
input: I,
) -> VarDiff<impl Data<Dim = Ix3> + Forward, impl Gradient<Dim = Ix3> + Overwrite + Backward>
where
I: ConvolveWithGroups<I, Learnable<Ix3>, Pad>,
I::Output: Into<VarDiff<T, U>>,
T: Data<Dim = Ix3>,
U: Gradient<Dim = Ix3> + Overwrite,
{
I::convolve_with_groups(
input,
self.weight.clone(),
&[self.stride],
&[self.dilation],
&[self.padding],
self.padding_mode.clone(),
self.groups,
)
.into()
+ self.bias.clone()
}
}
impl<Pad: PaddingMode> Register for GroupedConv1d<Pad> {
/// Registers the weight and the bias of this `GroupedConv1d` instance.
fn register_params(&self, params: &mut Vec<RawParam>) {
self.weight.register_params(params);
self.bias.register_params(params);
}
fn register_status(&mut self, _: Rc<Cell<bool>>) {}
}
/// Applies a **spatial convolution** over an input signal composed of several input planes.
///
/// See also [`GroupedConv2d`].
#[cfg_attr(feature = "serialize", derive(Serialize, Deserialize))]
pub struct Conv2d<Pad: PaddingMode> {
pub padding: (usize, usize),
pub padding_mode: Pad,
pub stride: (usize, usize),
pub dilation: (usize, usize),
pub weight: Learnable<Ix4>,
pub bias: Learnable<Ix1>,
}
impl<Pad: PaddingMode> Conv2d<Pad> {
/// Creates a new Conv2d.
///
/// # Arguments
///
/// * `in_channels` - number of planes in the input signal.
///
/// * `out_channels` - number of planes in the output signal.
///
/// * `kernel_size` - size of the kernel, a 2-tuple for this two-dimensional case.
///
/// * `padding` - padding to be applied to the input, a 2-tuple for this two-dimensional case.
///
/// * `padding_mode` - padding mode, it can be: [`Zero`], [`Constant`], [`Reflective`] or
/// [`Replicative`].
///
/// * `stride` - stride of the convolution, a 2-tuple for this two-dimensional case.
///
/// * `dilation` - controls the spacing between the kernel points, a 2-tuple for this
/// two-dimensional case.
///
/// The weight and the bias are initialized from *U(-k, k)* where
/// `k = (1. /(in_channels * kernel_w * kernel_h) as f32).sqrt()`.
pub fn new(
in_channels: usize,
out_channels: usize,
kernel_size: (usize, usize),
padding: (usize, usize),
padding_mode: Pad,
stride: (usize, usize),
dilation: (usize, usize),
) -> Self {
let (kernel_h, kernel_w) = kernel_size;
let weight = Input::new(Tensor::zeros((
out_channels,
in_channels,
kernel_h,
kernel_w,
)))
.requires_grad();
let bias = Input::new(Tensor::zeros(out_channels)).requires_grad();
let k = (1. / (in_channels * kernel_h * kernel_w) as f32).sqrt();
init::uniform(&weight, -k, k);
init::uniform(&bias, -k, k);
Self {
padding,
padding_mode,
stride,
dilation,
weight,
bias,
}
}
/// Computes a 2-dimensional convolution *(cross correlation)*.
///
/// # Arguments
///
/// `input` - the signal to convolve.
///
/// The **input** must be of shape *(N, Cin, H, W)*
/// * **N** is the batch size
/// * **Cin** is the number of input channels
/// * **H** is the **height** of the input
/// * **W** is the **width** of the input
///
/// The **kernel** must be of shape *(Cout, Cin, Hk, Wk)*
/// * **Cout** is the number of output channels
/// * **Cin** is the number of input channels
/// * **Hk** is the **height** of the kernel
/// * **Wk** is the **width** of the kernel
///
/// The resulting output shape will be *(N, Cout, Hout, Wout)*
pub fn forward<I, T, U>(
&self,
input: I,
) -> VarDiff<impl Data<Dim = Ix4> + Forward, impl Gradient<Dim = Ix4> + Overwrite + Backward>
where
I: Convolve<I, Learnable<Ix4>, Pad>,
I::Output: Into<VarDiff<T, U>>,
T: Data<Dim = Ix4>,
U: Gradient<Dim = Ix4> + Overwrite,
{
let (stride_h, stride_w) = self.stride;
let (padding_h, padding_w) = self.padding;
let (dilation_h, dilation_w) = self.dilation;
I::convolve(
input,
self.weight.clone(),
&[stride_h, stride_w],
&[dilation_h, dilation_w],
&[padding_h, padding_w],
self.padding_mode.clone(),
)
.into()
+ self.bias.clone()
}
}
impl<Pad: PaddingMode> Register for Conv2d<Pad> {
/// Registers the weight and the bias of this `Conv2d` instance.
fn register_params(&self, params: &mut Vec<RawParam>) {
self.weight.register_params(params);
self.bias.register_params(params);
}
fn register_status(&mut self, _: Rc<Cell<bool>>) {}
}
/// Applies a **spatial grouped convolution** over an input signal composed of several input planes.
#[cfg_attr(feature = "serialize", derive(Serialize, Deserialize))]
pub struct GroupedConv2d<Pad: PaddingMode> {
pub padding: (usize, usize),
pub padding_mode: Pad,
pub stride: (usize, usize),
pub dilation: (usize, usize),
pub groups: usize,
pub weight: Learnable<Ix4>,
pub bias: Learnable<Ix1>,
}
impl<Pad: PaddingMode> GroupedConv2d<Pad> {
/// Creates a new GroupedConv2d.
///
/// # Arguments
///
/// * `in_channels` - number of planes in the input signal.
///
/// * `out_channels` - number of planes in the output signal.
///
/// * `kernel_size` - size of the kernel, a 2-tuple for this two-dimensional case.
///
/// * `padding` - padding to be applied to the input, a 2-tuple for this two-dimensional case.
///
/// * `padding_mode` - padding mode, it can be: [`Zero`], [`Constant`], [`Reflective`] or
/// [`Replicative`].
///
/// * `stride` - stride of the convolution, a 2-tuple for this two-dimensional case.
///
/// * `dilation` - controls the spacing between the kernel points, a 2-tuple for this
/// two-dimensional case.
///
/// * `groups` - controls the connections between inputs and outputs. `in_channels` and
/// `out_channels` must both be divisible by groups.
///
/// For example:
/// * at `groups = 1`, all inputs are convolved to all outputs.
/// * at `groups = 2`, the operation becomes equivalent to having two convolutional layers
/// side by side, each seeing half the input channels and producing half the output channels,
/// and both subsequently concatenated.
/// * at `groups = in_channels`, each input channel is convolved with its own set of filters.
///
/// The weight and the bias of the layer are initialized from *U(-k, k)* where
/// `k = (groups /(in_channels * kernel_h * kernel_w) as f32).sqrt()`.
#[allow(clippy::too_many_arguments)]
pub fn new(
in_channels: usize,
out_channels: usize,
kernel_size: (usize, usize),
padding: (usize, usize),
padding_mode: Pad,
stride: (usize, usize),
dilation: (usize, usize),
groups: usize,
) -> Self {
let (kernel_h, kernel_w) = kernel_size;
let weight = Input::new(Tensor::zeros((
out_channels,
in_channels,
kernel_h,
kernel_w,
)))
.requires_grad();
let bias = Input::new(Tensor::zeros(out_channels)).requires_grad();
let k = (groups as f32 / (in_channels * kernel_h * kernel_w) as f32).sqrt();
init::uniform(&weight, -k, k);
init::uniform(&bias, -k, k);
Self {
padding,
padding_mode,
stride,
dilation,
groups,
weight,
bias,
}
}
/// Computes a 2-dimensional grouped convolution *(cross correlation)*.
///
/// # Arguments
///
/// `input` - the signal to convolve.
///
/// The **input** must be of shape *(N, Cin, H, W)*
/// * **N** is the batch size
/// * **Cin** is the number of input channels
/// * **H** is the **height** of the input
/// * **W** is the **width** of the input
///
/// The **kernel** must be of shape *(Cout, Cin, Hk, Wk)*
/// * **Cout** is the number of output channels
/// * **Cin** is the number of input channels
/// * **Hk** is the **height** of the kernel
/// * **Wk** is the **width** of the kernel
///
/// The resulting output shape will be *(N, Cout, Hout, Wout)*
pub fn forward<I, T, U>(
&self,
input: I,
) -> VarDiff<impl Data<Dim = Ix4> + Forward, impl Gradient<Dim = Ix4> + Overwrite + Backward>
where
I: ConvolveWithGroups<I, Learnable<Ix4>, Pad>,
I::Output: Into<VarDiff<T, U>>,
T: Data<Dim = Ix4>,
U: Gradient<Dim = Ix4> + Overwrite,
{
let (stride_h, stride_w) = self.stride;
let (padding_h, padding_w) = self.padding;
let (dilation_h, dilation_w) = self.dilation;
I::convolve_with_groups(
input,
self.weight.clone(),
&[stride_h, stride_w],
&[dilation_h, dilation_w],
&[padding_h, padding_w],
self.padding_mode.clone(),
self.groups,
)
.into()
+ self.bias.clone()
}
}
impl<Pad: PaddingMode> Register for GroupedConv2d<Pad> {
/// Registers the weight and the bias of this `GroupedConv2d` instance.
fn register_params(&self, params: &mut Vec<RawParam>) {
self.weight.register_params(params);
self.bias.register_params(params);
}
fn register_status(&mut self, _: Rc<Cell<bool>>) {}
}
/// Applies a **volumetric convolution** over an input signal composed of several input planes.
///
/// See also [`GroupedConv3d`].
#[cfg_attr(feature = "serialize", derive(Serialize, Deserialize))]
pub struct Conv3d<Pad: PaddingMode> {
pub padding: (usize, usize, usize),
pub padding_mode: Pad,
pub stride: (usize, usize, usize),
pub dilation: (usize, usize, usize),
pub weight: Learnable<Ix5>,
pub bias: Learnable<Ix1>,
}
impl<Pad: PaddingMode> Conv3d<Pad> {
/// Creates a new Conv3d.
///
/// # Arguments
///
/// * `in_channels` - number of planes in the input signal.
///
/// * `out_channels` - number of planes in the output signal.
///
/// * `kernel_size` - size of the kernel, a 3-tuple for this three-dimensional case.
///
/// * `padding` - padding to be applied to the input, a 3-tuple for this three-dimensional case.
///
/// * `padding_mode` - padding mode, it can be: [`Zero`], [`Constant`], [`Reflective`] or
/// [`Replicative`].
///
/// * `stride` - stride of the convolution, a 3-tuple for this three-dimensional case.
///
/// * `dilation` - controls the spacing between the kernel points, a 3-tuple for this
/// three-dimensional case.
///
/// The weight and the bias of the layer are initialized from *U(-k, k)* where
/// `k = (1. /(in_channels * kernel_d * kernel_w * kernel_h) as f32).sqrt()`.
pub fn new(
in_channels: usize,
out_channels: usize,
kernel_size: (usize, usize, usize),
padding: (usize, usize, usize),
padding_mode: Pad,
stride: (usize, usize, usize),
dilation: (usize, usize, usize),
) -> Self {
let (kernel_d, kernel_h, kernel_w) = kernel_size;
let weight = Input::new(Tensor::zeros((
out_channels,
in_channels,
kernel_d,
kernel_h,
kernel_w,
)))
.requires_grad();
let bias = Input::new(Tensor::zeros(out_channels)).requires_grad();
let k = (1. / (in_channels * kernel_d * kernel_h * kernel_w) as f32).sqrt();
init::uniform(&weight, -k, k);
init::uniform(&bias, -k, k);
Self {
padding,
padding_mode,
stride,
dilation,
weight,
bias,
}
}
/// Computes a 3-dimensional convolution *(cross correlation)*.
///
/// # Arguments
///
/// `input` - signal to convolve.
///
/// The **input** must be of shape *(N, Cin, D, H, W)*
/// * **N** is the batch size
/// * **Cin** is the number of input channels
/// * **D** is the **depth** of the input
/// * **H** is the **height** of the input
/// * **W** is the **width** of the input
///
/// The **kernel** must be of shape *(Cout, Cin, Dk, Hk, Wk)*
/// * **Cout** is the number of output channels
/// * **Cin** is the number of input channels
/// * **Dk** is the **depth** of the kernel
/// * **Hk** is the **height** of the kernel
/// * **Wk** is the **width** of the kernel
///
/// The resulting output shape will be *(N, Cout, Dout, Hout, Wout)*
pub fn forward<I, T, U>(
&self,
input: I,
) -> VarDiff<impl Data<Dim = Ix5> + Forward, impl Gradient<Dim = Ix5> + Overwrite + Backward>
where
I: Convolve<I, Learnable<Ix5>, Pad>,
I::Output: Into<VarDiff<T, U>>,
T: Data<Dim = Ix5>,
U: Gradient<Dim = Ix5> + Overwrite,
{
let (stride_d, stride_h, stride_w) = self.stride;
let (padding_d, padding_h, padding_w) = self.padding;
let (dilation_d, dilation_h, dilation_w) = self.dilation;
I::convolve(
input,
self.weight.clone(),
&[stride_d, stride_h, stride_w],
&[dilation_d, dilation_h, dilation_w],
&[padding_d, padding_h, padding_w],
self.padding_mode.clone(),
)
.into()
+ self.bias.clone()
}
}
impl<Pad: PaddingMode> Register for Conv3d<Pad> {
/// Registers the weight and the bias of this `Conv3d` instance.
fn register_params(&self, params: &mut Vec<RawParam>) {
self.weight.register_params(params);
self.bias.register_params(params);
}
fn register_status(&mut self, _: Rc<Cell<bool>>) {}
}
/// Applies a **grouped volumetric convolution** over an input signal composed of several input
/// planes.
#[cfg_attr(feature = "serialize", derive(Serialize, Deserialize))]
pub struct GroupedConv3d<Pad: PaddingMode> {
pub padding: (usize, usize, usize),
pub padding_mode: Pad,
pub stride: (usize, usize, usize),
pub dilation: (usize, usize, usize),
pub groups: usize,
pub weight: Learnable<Ix5>,
pub bias: Learnable<Ix1>,
}
impl<Pad: PaddingMode> GroupedConv3d<Pad> {
/// Creates a new GroupedConv3d.
///
/// # Arguments
///
/// * `in_channels` - number of planes in the input signal.
///
/// * `out_channels` - number of planes in the output signal.
///
/// * `kernel_size` - size of the kernel, a 3-tuple for this three-dimensional case.
///
/// * `padding` - padding to be applied to the input, a 3-tuple for this three-dimensional case.
///
/// * `padding_mode` - padding mode, it can be: [`Zero`], [`Constant`], [`Reflective`] or
/// [`Replicative`].
///
/// * `stride` - stride of the convolution, a 3-tuple for this three-dimensional case.
///
/// * `dilation` - controls the spacing between the kernel points, a 3-tuple for this
/// three-dimensional case.
///
/// * `groups` - controls the connections between inputs and outputs. `in_channels` and
/// `out_channels` must both be divisible by groups.
///
/// For example:
/// * at `groups = 1`, all inputs are convolved to all outputs.
/// * at `groups = 2`, the operation becomes equivalent to having two convolutional layers
/// side by side, each seeing half the input channels and producing half the output channels,
/// and both subsequently concatenated.
/// * at `groups = in_channels`, each input channel is convolved with its own set of filters.
///
/// The weight and the bias are initialized from *U(-k, k)* where
/// `k = (groups /(in_channels * kernel_d * kernel_h * kernel_w) as f32).sqrt()`.
#[allow(clippy::too_many_arguments)]
pub fn new(
in_channels: usize,
out_channels: usize,
kernel_size: (usize, usize, usize),
padding: (usize, usize, usize),
padding_mode: Pad,
stride: (usize, usize, usize),
dilation: (usize, usize, usize),
groups: usize,
) -> Self {
let (kernel_d, kernel_h, kernel_w) = kernel_size;
let weight = Input::new(Tensor::zeros((
out_channels,
in_channels,
kernel_d,
kernel_h,
kernel_w,
)))
.requires_grad();
let bias = Input::new(Tensor::zeros(out_channels)).requires_grad();
let k = (1. / (in_channels * kernel_d * kernel_h * kernel_w) as f32).sqrt();
init::uniform(&weight, -k, k);
init::uniform(&bias, -k, k);
Self {
padding,
padding_mode,
stride,
dilation,
groups,
weight,
bias,
}
}
/// Computes a 3-dimensional grouped convolution *(cross correlation)*.
///
/// `input` - the signal to convolve.
///
/// The **input** must be of shape *(N, Cin, D, H, W)*
/// * **N** is the batch size
/// * **Cin** is the number of input channels
/// * **D** is the **depth** of the input
/// * **H** is the **height** of the input
/// * **W** is the **width** of the input
///
/// The **kernel** must be of shape *(Cout, Cin, Dk, Hk, Wk)*
/// * **Cout** is the number of output channels
/// * **Cin** is the number of input channels
/// * **Dk** is the **depth** of the kernel
/// * **Hk** is the **height** of the kernel
/// * **Wk** is the **width** of the kernel
///
/// The resulting output shape will be *(N, Cout, Dout, Hout, Wout)*
pub fn forward<I, T, U>(
&self,
input: I,
) -> VarDiff<impl Data<Dim = Ix5> + Forward, impl Gradient<Dim = Ix5> + Overwrite + Backward>
where
I: ConvolveWithGroups<I, Learnable<Ix5>, Pad>,
I::Output: Into<VarDiff<T, U>>,
T: Data<Dim = Ix5>,
U: Gradient<Dim = Ix5> + Overwrite,
{
let (stride_d, stride_h, stride_w) = self.stride;
let (padding_d, padding_h, padding_w) = self.padding;
let (dilation_d, dilation_h, dilation_w) = self.dilation;
I::convolve_with_groups(
input,
self.weight.clone(),
&[stride_d, stride_h, stride_w],
&[dilation_d, dilation_h, dilation_w],
&[padding_d, padding_h, padding_w],
self.padding_mode.clone(),
self.groups,
)
.into()
+ self.bias.clone()
}
}
impl<Pad: PaddingMode> Register for GroupedConv3d<Pad> {
/// Registers the weight and the bias of this `GroupedConv3d` instance.
fn register_params(&self, params: &mut Vec<RawParam>) {
self.weight.register_params(params);
self.bias.register_params(params);
}
fn register_status(&mut self, _: Rc<Cell<bool>>) {}
}