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crates/burn-import/onnx-tests/tests/avg_pool1d/avg_pool1d.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,58 @@ | ||
| #!/usr/bin/env python3 | ||
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| # used to generate model: avg_pool1d.onnx | ||
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| import torch | ||
| import torch.nn as nn | ||
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| class Model(nn.Module): | ||
| def __init__(self): | ||
| super(Model, self).__init__() | ||
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| self.pool1 = nn.AvgPool1d(4, stride=2) | ||
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| self.pool2 = nn.AvgPool1d(4, stride=2, padding=2, count_include_pad=True) | ||
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| self.pool3 = nn.AvgPool1d(4, stride=2, padding=2, count_include_pad=False) | ||
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| def forward(self, x1, x2, x3): | ||
| y1 = self.pool1(x1) | ||
| y2 = self.pool2(x2) | ||
| y3 = self.pool3(x3) | ||
| return y1, y2, y3 | ||
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| def main(): | ||
| # Set seed for reproducibility | ||
| torch.manual_seed(1) | ||
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| # Print options | ||
| torch.set_printoptions(precision=3) | ||
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| # Export to onnx | ||
| model = Model() | ||
| model.eval() | ||
| device = torch.device("cpu") | ||
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| file_name = "avg_pool1d.onnx" | ||
| input1 = torch.randn(1, 5, 5, device=device) | ||
| torch.onnx.export(model, (input1, input1, input1), file_name, | ||
| verbose=False, opset_version=16) | ||
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| print("Finished exporting model to {}".format(file_name)) | ||
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| # Output some test data for use in the test | ||
| print("Test input data shape: {}".format(input1.shape)) | ||
| print("Test input data: {}".format(input1)) | ||
| output1, output2, output3 = model.forward(input1, input1, input1) | ||
| print("Test output1 data shape: {}".format(output1.shape)) | ||
| print("Test output2 data shape: {}".format(output2.shape)) | ||
| print("Test output3 data shape: {}".format(output3.shape)) | ||
| print("Test output1: {}".format(output1)) | ||
| print("Test output2: {}".format(output2)) | ||
| print("Test output3: {}".format(output3)) | ||
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| if __name__ == '__main__': | ||
| main() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,155 @@ | ||
| use proc_macro2::TokenStream; | ||
| use quote::quote; | ||
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| use burn::{nn::pool::AvgPool1dConfig, record::PrecisionSettings}; | ||
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| use super::{Node, NodeCodegen}; | ||
| use crate::burn::{BurnImports, OtherType, Scope, TensorType, ToTokens, Type}; | ||
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| #[derive(Debug, Clone)] | ||
| pub struct AvgPool1dNode { | ||
| pub field: OtherType, | ||
| pub input: TensorType, | ||
| pub output: TensorType, | ||
| pub config: AvgPool1dConfig, | ||
| } | ||
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| impl AvgPool1dNode { | ||
| pub fn new<S: AsRef<str>>( | ||
| name: S, | ||
| input: TensorType, | ||
| output: TensorType, | ||
| config: AvgPool1dConfig, | ||
| ) -> Self { | ||
| Self { | ||
| field: OtherType::new( | ||
| name, | ||
| quote! { | ||
| AvgPool1d | ||
| }, | ||
| ), | ||
| input, | ||
| output, | ||
| config, | ||
| } | ||
| } | ||
| } | ||
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| impl<PS: PrecisionSettings> NodeCodegen<PS> for AvgPool1dNode { | ||
| fn input_types(&self) -> Vec<Type> { | ||
| vec![Type::Tensor(self.input.clone())] | ||
| } | ||
| fn output_types(&self) -> Vec<Type> { | ||
| vec![Type::Tensor(self.output.clone())] | ||
| } | ||
| 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; | ||
| 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; | ||
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| let tokens = quote! { | ||
| let #name = AvgPool1dConfig::new(#kernel_size) | ||
| .with_stride(#strides) | ||
| .with_padding(#padding) | ||
| .with_count_include_pad(#count_include_pad) | ||
| .init(); | ||
| }; | ||
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| Some(tokens) | ||
| } | ||
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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 = &self.output.name; | ||
| let field = &self.field.name; | ||
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| quote! { | ||
| let #output = self.#field.forward(#input); | ||
| } | ||
| } | ||
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| fn register_imports(&self, imports: &mut BurnImports) { | ||
| imports.register("burn::nn::PaddingConfig1d"); | ||
| imports.register("burn::nn::pool::AvgPool1d"); | ||
| imports.register("burn::nn::pool::AvgPool1dConfig"); | ||
| } | ||
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| fn into_node(self) -> Node<PS> { | ||
| Node::AvgPool1d(self) | ||
| } | ||
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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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| #[cfg(test)] | ||
| mod tests { | ||
| use super::*; | ||
| use crate::burn::{graph::BurnGraph, node::test::assert_tokens, TensorType}; | ||
| use burn::{nn::PaddingConfig1d, record::FullPrecisionSettings}; | ||
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| #[test] | ||
| fn test_codegen() { | ||
| let mut graph = BurnGraph::<FullPrecisionSettings>::default(); | ||
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| graph.register(AvgPool1dNode::new( | ||
| "avg_pool1d", | ||
| TensorType::new_float("input", 3), | ||
| TensorType::new_float("output", 3), | ||
| AvgPool1dConfig::new(3) | ||
| .with_stride(1) | ||
| .with_padding(PaddingConfig1d::Valid), | ||
| )); | ||
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| graph.register_input_output(vec!["input".to_string()], vec!["output".to_string()]); | ||
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| let expected = quote! { | ||
| use burn::{ | ||
| module::Module, | ||
| tensor::{backend::Backend, Tensor}, | ||
| }; | ||
| use burn::nn::PaddingConfig1d; | ||
| use burn::nn::pool::AvgPool1d; | ||
| use burn::nn::pool::AvgPool1dConfig; | ||
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| #[derive(Module, Debug)] | ||
| pub struct Model <B: Backend> { | ||
| avg_pool1d: AvgPool1d, | ||
| 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 avg_pool1d = AvgPool1dConfig::new(3) | ||
| .with_stride(1) | ||
| .with_padding(PaddingConfig1d::Valid) | ||
| .with_count_include_pad(true) | ||
| .init(); | ||
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| Self { | ||
| avg_pool1d, | ||
| 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, 3>) -> Tensor<B, 3> { | ||
| let output = self.avg_pool1d.forward(input); | ||
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| output | ||
| } | ||
| } | ||
| }; | ||
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| assert_tokens(graph.codegen(), expected); | ||
| } | ||
| } |
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