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* feat: resize onnx import * fix: resize import proc macro output * fix: lint * fix: simplify resize onnx * fix: onnx-tests passing * feedback: remove dead code and resolve merge conflicts
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,35 @@ | ||
| #!/usr/bin/env python3 | ||
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| # used to generate model: onnx-tests/tests/resize/resize.onnx | ||
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| import onnx | ||
| from onnx import helper, TensorProto | ||
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| def main() -> None: | ||
| input_tensor = helper.make_tensor_value_info("input_tensor", TensorProto.FLOAT, [1, 1, 4, 4]) | ||
| sizes_tensor = helper.make_tensor_value_info("sizes", TensorProto.INT64, [4]) | ||
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| resize_node = helper.make_node( | ||
| "Resize", | ||
| name="resize_node", | ||
| inputs=["input_tensor", "", "", "sizes"], | ||
| outputs=["output"], | ||
| mode="linear", | ||
| ) | ||
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| graph_def = helper.make_graph( | ||
| nodes=[resize_node], | ||
| name="ResizeGraph", | ||
| inputs=[input_tensor, sizes_tensor], | ||
| outputs=[ | ||
| helper.make_tensor_value_info("output", TensorProto.FLOAT, [1, 1, 2, 2]) | ||
| ], | ||
| ) | ||
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| model_def = helper.make_model(graph_def, producer_name="resize") | ||
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| onnx.save(model_def, "resize.onnx") | ||
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| if __name__ == "__main__": | ||
| main() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,207 @@ | ||
| use super::{Node, NodeCodegen}; | ||
| use crate::burn::{OtherType, Scope, TensorType, Type}; | ||
| use burn::module::Module; | ||
| use burn::record::PrecisionSettings; | ||
| use proc_macro2::TokenStream; | ||
| use quote::quote; | ||
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| #[derive(Module, Debug, Clone)] | ||
| pub enum ResizeMode { | ||
| Nearest, | ||
| Linear, | ||
| Cubic, | ||
| } | ||
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| #[derive(new, Module, Debug, Clone)] | ||
| pub struct ResizeOptions { | ||
| pub mode: ResizeMode, | ||
| } | ||
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| #[derive(Debug, Clone)] | ||
| pub struct ResizeNode { | ||
| pub field: OtherType, | ||
| pub input: TensorType, | ||
| pub output: TensorType, | ||
| pub output_size: TensorType, | ||
| pub config: ResizeOptions, | ||
| } | ||
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| impl ResizeNode { | ||
| pub fn new<S: AsRef<str>>( | ||
| name: S, | ||
| input: TensorType, | ||
| output: TensorType, | ||
| output_size: TensorType, | ||
| config: ResizeOptions, | ||
| ) -> Self { | ||
| Self { | ||
| field: OtherType::new( | ||
| name, | ||
| quote! { | ||
| burn::module::Ignored<InterpolateOptions> | ||
| }, | ||
| ), | ||
| input, | ||
| output, | ||
| output_size, | ||
| config, | ||
| } | ||
| } | ||
| } | ||
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| impl<PS: PrecisionSettings> NodeCodegen<PS> for ResizeNode { | ||
| fn output_types(&self) -> Vec<Type> { | ||
| vec![Type::Tensor(self.output.clone())] | ||
| } | ||
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| fn input_types(&self) -> Vec<Type> { | ||
| vec![ | ||
| Type::Tensor(self.input.clone()), | ||
| Type::Tensor(self.output_size.clone()), | ||
| ] | ||
| } | ||
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| fn field_type(&self) -> Option<Type> { | ||
| Some(Type::Other(self.field.clone())) | ||
| } | ||
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| fn field_init(&self) -> Option<TokenStream> { | ||
| let name = &self.field.name; | ||
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| let mode = match self.config.mode { | ||
| ResizeMode::Linear => quote! { InterpolateMode::Bilinear }, | ||
| ResizeMode::Nearest => quote! { InterpolateMode::Nearest }, | ||
| ResizeMode::Cubic => quote! { InterpolateMode::Bicubic }, | ||
| }; | ||
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| let tokens = quote! { | ||
| let #name = InterpolateOptions { | ||
| mode: #mode, | ||
| }; | ||
| let #name = burn::module::Ignored(#name); | ||
| }; | ||
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| Some(tokens) | ||
| } | ||
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| fn field_serialize<S: serde::Serializer>(&self, serializer: S) -> Result<S::Ok, S::Error> { | ||
| S::serialize_none(serializer) | ||
| } | ||
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| fn forward(&self, scope: &mut Scope, node_position: usize) -> TokenStream { | ||
| let input = scope.tensor_use_owned(&self.input, node_position); | ||
| let output_size = scope.tensor_use_owned(&self.output_size, node_position); | ||
| let output = &self.output.name; | ||
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| let field = &self.field.name; | ||
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| quote! { | ||
| let output_size_raw = #output_size.to_data().value; | ||
| let mut output_size = [0usize; 2]; | ||
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| for (i, &x) in output_size_raw.iter().rev().take(2).rev().enumerate() { | ||
| output_size[i] = x.elem::<i64>() as usize; | ||
| } | ||
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| let #output = interpolate( | ||
| #input, | ||
| output_size, | ||
| self.#field.0.clone(), | ||
| ); | ||
| } | ||
| } | ||
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| fn into_node(self) -> Node<PS> { | ||
| Node::Resize(self) | ||
| } | ||
|
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| fn register_imports(&self, imports: &mut crate::burn::BurnImports) { | ||
| imports.register("burn::tensor::ElementConversion"); | ||
| imports.register("burn::tensor::module::interpolate"); | ||
| imports.register("burn::tensor::ops::InterpolateMode"); | ||
| imports.register("burn::tensor::ops::InterpolateOptions"); | ||
| } | ||
| } | ||
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| #[cfg(test)] | ||
| mod tests { | ||
| use burn::record::FullPrecisionSettings; | ||
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| use super::*; | ||
| use crate::burn::{ | ||
| graph::BurnGraph, | ||
| node::{resize::ResizeNode, test::assert_tokens}, | ||
| TensorType, | ||
| }; | ||
|
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| #[test] | ||
| fn test_codegen_nodes() { | ||
| let mut graph = BurnGraph::<FullPrecisionSettings>::default(); | ||
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| graph.register(ResizeNode::new( | ||
| "resize", | ||
| TensorType::new_float("tensor1", 4), | ||
| TensorType::new_float("tensor2", 4), | ||
| TensorType::new_int("output_size", 1), | ||
| ResizeOptions::new(ResizeMode::Linear), | ||
| )); | ||
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| graph.register_input_output( | ||
| vec!["tensor1".to_string(), "output_size".to_string()], | ||
| vec!["tensor2".to_string()], | ||
| ); | ||
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| let expected = quote! { | ||
| use burn::tensor::module::interpolate; | ||
| use burn::tensor::ops::InterpolateMode; | ||
| use burn::tensor::ops::InterpolateOptions; | ||
| use burn::tensor::ElementConversion; | ||
| use burn::tensor::Int; | ||
| use burn::{ | ||
| module::Module, | ||
| tensor::{backend::Backend, Tensor}, | ||
| }; | ||
|
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| #[derive(Module, Debug)] | ||
| pub struct Model<B: Backend> { | ||
| resize: burn::module::Ignored<InterpolateOptions>, | ||
| phantom: core::marker::PhantomData<B>, | ||
| device: burn::module::Ignored<B::Device>, | ||
| } | ||
|
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| impl<B: Backend> Model <B> { | ||
| #[allow(unused_variables)] | ||
| pub fn new(device: &B::Device) -> Self { | ||
| let resize = InterpolateOptions { | ||
| mode: InterpolateMode::Bilinear, | ||
| }; | ||
| let resize = burn::module::Ignored(resize); | ||
| Self { | ||
| resize, | ||
| phantom: core::marker::PhantomData, | ||
| device: burn::module::Ignored(device.clone()), | ||
| } | ||
| } | ||
| #[allow(clippy::let_and_return, clippy::approx_constant)] | ||
| pub fn forward( | ||
| &self, | ||
| tensor1: Tensor<B, 4>, | ||
| output_size: Tensor<B, 1, Int> | ||
| ) -> Tensor<B, 4> { | ||
| let output_size_raw = output_size.to_data().value; | ||
| let mut output_size = [0usize; 2]; | ||
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| for (i, &x) in output_size_raw.iter().rev().take(2).rev().enumerate() { | ||
| output_size[i] = x.elem::<i64>() as usize; | ||
| } | ||
|
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| let tensor2 = interpolate(tensor1, output_size, self.resize.0.clone()); | ||
|
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| tensor2 | ||
| } | ||
| } | ||
| }; | ||
|
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| assert_tokens(graph.codegen(), expected); | ||
| } | ||
| } |
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