use std::str::FromStr;
use super::{Node, NodeCodegen};
use crate::burn::{Scope, TensorType, ToTokens, Type};
use burn::config::Config;
use burn::record::PrecisionSettings;
use proc_macro2::TokenStream;
use quote::quote;
#[derive(Config, Debug)]
pub struct PadConfig {
pub pads: Vec<usize>,
pub constant_value: f32,
}
#[derive(Debug, Clone, new)]
pub struct PadNode {
pub input: TensorType,
pub output: TensorType,
pub config: PadConfig,
}
impl<PS: PrecisionSettings> NodeCodegen<PS> for PadNode {
fn output_types(&self) -> Vec<Type> {
vec![Type::Tensor(self.output.clone())]
}
fn input_types(&self) -> Vec<Type> {
vec![Type::Tensor(self.input.clone())]
}
fn forward(&self, scope: &mut Scope, node_position: usize) -> TokenStream {
let input = scope.tensor_use_owned(&self.input, node_position);
let output = &self.output.name;
let pads = self.config.pads.iter().map(|p| p.to_tokens());
let constant_value_string = format!("{}_f32.elem()", self.config.constant_value);
let constant_value = TokenStream::from_str(&constant_value_string).unwrap();
quote! {
let #output = #input.pad((#(#pads),*), #constant_value);
}
}
fn into_node(self) -> Node<PS> {
Node::Pad(self)
}
fn register_imports(&self, imports: &mut crate::burn::BurnImports) {
imports.register("burn::tensor::ElementConversion");
}
}
#[cfg(test)]
mod tests {
use burn::record::FullPrecisionSettings;
use super::*;
use crate::burn::{
graph::BurnGraph,
node::{pad::PadNode, test::assert_tokens},
TensorType,
};
#[test]
fn test_codegen_pad() {
let mut graph = BurnGraph::<FullPrecisionSettings>::default();
let config = PadConfig::new(vec![1, 2, 3, 4], -1.0);
graph.register(PadNode::new(
TensorType::new_float("input", 2),
TensorType::new_float("output", 2),
config,
));
graph.register_input_output(vec!["input".to_string()], vec!["output".to_string()]);
let expected = quote! {
use burn::tensor::ElementConversion;
use burn::{
module::Module,
tensor::{backend::Backend, Tensor},
};
#[derive(Module, Debug)]
pub struct Model<B: Backend> {
phantom: core::marker::PhantomData<B>,
device: burn::module::Ignored<B::Device>,
}
impl<B: Backend> Model <B> {
#[allow(unused_variables)]
pub fn new(device: &B::Device) -> Self {
Self {
phantom: core::marker::PhantomData,
device: burn::module::Ignored(device.clone()),
}
}
#[allow(clippy::let_and_return, clippy::approx_constant)]
pub fn forward(&self, input: Tensor<B, 2>) -> Tensor<B, 2> {
let output = input.pad((1, 2, 3, 4), -1_f32.elem());
output
}
}
};
assert_tokens(graph.codegen(), expected);
}
}