burn-import 0.20.1

Library for importing datamodels into the Burn framework
Documentation 
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use super::prelude::*;
impl NodeCodegen for onnx_ir::node::avg_pool1d::AveragePool1dNode {
    fn inputs(&self) -> &[Argument] {
        &self.inputs
    }

    fn outputs(&self) -> &[Argument] {
        &self.outputs
    }

    fn field(&self) -> Option<Field> {
        let name = Ident::new(&self.name, Span::call_site());
        let kernel_size = self.config.kernel_size.to_tokens();
        let strides = self.config.stride.to_tokens();
        let padding = self.config.padding.to_tokens();
        let count_include_pad = self.config.count_include_pad;
        let ceil_mode = self.config.ceil_mode;

        Some(Field::new(
            self.name.clone(),
            quote! {
                AvgPool1d
            },
            quote! {
                let #name = AvgPool1dConfig::new(#kernel_size)
                    .with_stride(#strides)
                    .with_padding(#padding)
                    .with_count_include_pad(#count_include_pad)
                    .with_ceil_mode(#ceil_mode)
                    .init();
            },
        ))
    }

    fn forward(&self, scope: &mut ScopeAtPosition<'_>) -> TokenStream {
        let input = scope.arg(self.inputs.first().unwrap());
        let output = arg_to_ident(self.outputs.first().unwrap());
        let field = Ident::new(&self.name, Span::call_site());

        quote! {
            let #output = self.#field.forward(#input);
        }
    }

    fn register_imports(&self, imports: &mut BurnImports) {
        imports.register("burn::nn::pool::AvgPool1d");
        imports.register("burn::nn::pool::AvgPool1dConfig");
        imports.register("burn::nn::PaddingConfig1d");
    }
}

#[cfg(test)]
mod tests {
    use super::super::test_helpers::*;
    use burn::tensor::DType;
    use insta::assert_snapshot;
    use onnx_ir::node::avg_pool1d::{AveragePool1dNode, AveragePool1dNodeBuilder, AvgPool1dConfig};
    use onnx_ir::padding::PaddingConfig1d;

    fn create_avg_pool1d_node(name: &str, ceil_mode: bool) -> AveragePool1dNode {
        let config = AvgPool1dConfig::new(3, 1, PaddingConfig1d::Valid, false, 1, ceil_mode);

        AveragePool1dNodeBuilder::new(name)
            .input_tensor("input", 3, DType::F32)
            .output_tensor("output", 3, DType::F32)
            .config(config)
            .build()
    }

    #[test]
    fn test_avg_pool1d_forward() {
        let node = create_avg_pool1d_node("pool1", false);
        let code = codegen_forward_default(&node);
        assert_snapshot!(code, @r"
        pub fn forward(&self, input: Tensor<B, 3>) -> Tensor<B, 3> {
            let output = self.pool1.forward(input);
            output
        }
        ");
    }

    #[test]
    fn test_avg_pool1d_forward_with_clone() {
        let node = create_avg_pool1d_node("pool1", false);
        let code = codegen_forward_with_clone(&node);
        assert_snapshot!(code, @r"
        pub fn forward(&self, input: Tensor<B, 3>) -> Tensor<B, 3> {
            let output = self.pool1.forward(input.clone());
            output
        }
        ");
    }

    #[test]
    fn test_avg_pool1d_field_init_ceil_mode_false() {
        let node = create_avg_pool1d_node("pool1", false);
        let code = codegen_field_init(&node);
        assert_snapshot!(code, @r#"
        let pool1 = AvgPool1dConfig::new(3)
            .with_stride(1)
            .with_padding(PaddingConfig1d::Valid)
            .with_count_include_pad(false)
            .with_ceil_mode(false)
            .init();
        "#);
    }

    #[test]
    fn test_avg_pool1d_field_init_ceil_mode_true() {
        let node = create_avg_pool1d_node("pool1", true);
        let code = codegen_field_init(&node);
        assert_snapshot!(code, @r#"
        let pool1 = AvgPool1dConfig::new(3)
            .with_stride(1)
            .with_padding(PaddingConfig1d::Valid)
            .with_count_include_pad(false)
            .with_ceil_mode(true)
            .init();
        "#);
    }
}