candle/candle-nn/src/conv.rs

//! Convolution Layers.
use candle::{Result, Tensor};

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub struct Conv1dConfig {
    pub padding: usize,
    pub stride: usize,
}

impl Default for Conv1dConfig {
    fn default() -> Self {
        Self {
            padding: 0,
            stride: 1,
        }
    }
}

#[derive(Debug)]
pub struct Conv1d {
    weight: Tensor,
    bias: Option<Tensor>,
    config: Conv1dConfig,
}

impl Conv1d {
    pub fn new(weight: Tensor, bias: Option<Tensor>, config: Conv1dConfig) -> Self {
        Self {
            weight,
            bias,
            config,
        }
    }

    pub fn config(&self) -> &Conv1dConfig {
        &self.config
    }

    pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
        let x = x.conv1d(&self.weight, self.config.padding, self.config.stride)?;
        match &self.bias {
            None => Ok(x),
            Some(bias) => {
                let b = bias.dims1()?;
                let bias = bias.reshape((1, b, 1))?;
                Ok(x.broadcast_add(&bias)?)
            }
        }
    }
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub struct Conv2dConfig {
    pub padding: usize,
    pub stride: usize,
}

impl Default for Conv2dConfig {
    fn default() -> Self {
        Self {
            padding: 0,
            stride: 1,
        }
    }
}

#[allow(dead_code)]
#[derive(Debug)]
pub struct Conv2d {
    weight: Tensor,
    bias: Option<Tensor>,
    config: Conv2dConfig,
}

impl Conv2d {
    pub fn new(weight: Tensor, bias: Option<Tensor>, config: Conv2dConfig) -> Self {
        Self {
            weight,
            bias,
            config,
        }
    }

    pub fn config(&self) -> &Conv2dConfig {
        &self.config
    }

    pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
        let x = x.conv2d(&self.weight, self.config.padding, self.config.stride)?;
        match &self.bias {
            None => Ok(x),
            Some(bias) => {
                let b = bias.dims1()?;
                let bias = bias.reshape((1, b, 1, 1))?;
                Ok(x.broadcast_add(&bias)?)
            }
        }
    }
}

pub fn conv1d(
    in_channels: usize,
    out_channels: usize,
    kernel_size: usize,
    cfg: Conv1dConfig,
    vs: crate::VarBuilder,
) -> Result<Conv1d> {
    let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
    let ws = vs.get_or_init((out_channels, in_channels, kernel_size), "weight", init_ws)?;
    let bound = 1. / (in_channels as f64).sqrt();
    let init_bs = crate::Init::Uniform {
        lo: -bound,
        up: bound,
    };
    let bs = vs.get_or_init(out_channels, "bias", init_bs)?;
    Ok(Conv1d::new(ws, Some(bs), cfg))
}

pub fn conv2d(
    in_channels: usize,
    out_channels: usize,
    kernel_size: usize,
    cfg: Conv2dConfig,
    vs: crate::VarBuilder,
) -> Result<Conv2d> {
    let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
    let ws = vs.get_or_init(
        (out_channels, in_channels, kernel_size, kernel_size),
        "weight",
        init_ws,
    )?;
    let bound = 1. / (in_channels as f64).sqrt();
    let init_bs = crate::Init::Uniform {
        lo: -bound,
        up: bound,
    };
    let bs = vs.get_or_init(out_channels, "bias", init_bs)?;
    Ok(Conv2d::new(ws, Some(bs), cfg))
}