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* Implement ONNX pad * ONNX pad arguments fix pad now requires 2 or more arguments if the third argument is not given, it will default to 0 * fixing bug in input len fix * change panic comment Change panic comment from needing two inputs. This comes from the fact that the ONNX spec requires two necessary inputs but could have more two more optional argument. --------- Co-authored-by: JC <[email protected]> Co-authored-by: mepatrick73 <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,158 @@ | ||
| #!/usr/bin/env python3 | ||
|
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| # used to generate model: onnx-tests/tests/pad/pad.onnx | ||
|
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| ### Helper Functions ### | ||
| from pathlib import Path | ||
| from typing import Any | ||
| import numpy | ||
| from numpy.core.multiarray import dtype | ||
| import onnx | ||
| from onnx import ModelProto, TensorProto, ValueInfoProto | ||
| from onnx.reference import ReferenceEvaluator | ||
| from onnx.checker import check_model | ||
| from onnx.helper import ( | ||
| make_model, | ||
| make_node, | ||
| make_graph, | ||
| ) | ||
|
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|
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| def build_test_save( | ||
| name: str, | ||
| inputs: list[ValueInfoProto], | ||
| outputs: list[ValueInfoProto], | ||
| initializers: list[TensorProto] = [], | ||
| attributes: dict[str, Any] = {}, | ||
| ) -> None: | ||
| node_inputs = [input.name for input in inputs + initializers] | ||
| node_outputs = [output.name for output in outputs] | ||
|
|
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| node = make_node( | ||
| name.capitalize(), | ||
| inputs=node_inputs, | ||
| outputs=node_outputs, | ||
| **attributes, | ||
| ) | ||
|
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| graph = make_graph( | ||
| nodes=[node], | ||
| name=f"{name.capitalize()}Graph", | ||
| inputs=inputs, | ||
| outputs=outputs, | ||
| initializer=initializers, | ||
| ) | ||
|
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| onnx_model = make_model(graph) | ||
| check_model(onnx_model) | ||
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| run_tests(onnx_model) | ||
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| onnx.save(onnx_model, Path(__file__).with_name(f"{name}.onnx")) | ||
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|
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| class TestCase: | ||
| def __init__( | ||
| self, name: str, feeds: dict[str, numpy.ndarray], expected: numpy.ndarray | ||
| ): | ||
| self.name = name | ||
| self.feeds = feeds | ||
| self.expected = expected | ||
|
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| def test_model(self, model: ModelProto): | ||
| sess = ReferenceEvaluator(model) | ||
|
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| result = numpy.array(sess.run(None, self.feeds)) | ||
|
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| if not numpy.array_equal(result, self.expected): | ||
| print( | ||
| f"""{self.name} | ||
| Expected result: {self.expected} | ||
| Got: {result}""" | ||
| ) | ||
| raise Exception("Test failed") | ||
|
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|
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| def test_positive_pads(model: ModelProto) -> None: | ||
| input_tensor = numpy.arange(1, 7, dtype="float32").reshape(3, 2) | ||
| pads = numpy.array([1, 2, 3, 4], dtype="int") | ||
| constant_value = 0.0 | ||
| feeds = { | ||
| "input_tensor": input_tensor, | ||
| "pads": pads, | ||
| "constant_value": constant_value, | ||
| } | ||
| expected = numpy.array( | ||
| [ | ||
| [ | ||
| [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], | ||
| [0.0, 0.0, 1.0, 2.0, 0.0, 0.0, 0.0, 0.0], | ||
| [0.0, 0.0, 3.0, 4.0, 0.0, 0.0, 0.0, 0.0], | ||
| [0.0, 0.0, 5.0, 6.0, 0.0, 0.0, 0.0, 0.0], | ||
| [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], | ||
| [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], | ||
| [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], | ||
| ] | ||
| ] | ||
| ) | ||
|
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| TestCase("test_positive_constant_pads", feeds, expected).test_model(model) | ||
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| def test_1d_input(model: ModelProto) -> None: | ||
| input_tensor = numpy.arange(1, 5, dtype="float32") | ||
| pads = numpy.array([1, 2], dtype="int") | ||
| constant_value = 0.0 | ||
| feeds = { | ||
| "input_tensor": input_tensor, | ||
| "pads": pads, | ||
| "constant_value": constant_value, | ||
| } | ||
| expected = numpy.array([[0.0, 1.0, 2.0, 3.0, 4.0, 0.0, 0.0]]) | ||
|
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| TestCase("test_1d_input", feeds, expected).test_model(model) | ||
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| def run_tests(model: ModelProto) -> None: | ||
| test_positive_pads(model) | ||
| test_1d_input(model) | ||
| # TODO: test_negative_pads | ||
| # TODO: support other modes: reflect, edge, wrap | ||
|
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| ### Helper Functions End ### | ||
|
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| import numpy | ||
| from onnx import TensorProto, numpy_helper | ||
| from onnx.helper import make_tensor_value_info | ||
|
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| def get_initializers() -> list[TensorProto]: | ||
| pads = numpy_helper.from_array( | ||
| numpy.array([1, 2, 3, 4]).astype(numpy.int64), name="pads" | ||
| ) | ||
| constant_value = numpy_helper.from_array( | ||
| numpy.array([0.0]).astype(numpy.float32), name="constant_value" | ||
| ) | ||
|
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| return [pads, constant_value] | ||
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| def main() -> None: | ||
| name = "pad" | ||
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| inputs = [make_tensor_value_info("input_tensor", TensorProto.FLOAT, [None, None])] | ||
| outputs = [make_tensor_value_info("output", TensorProto.FLOAT, [None, None])] | ||
| initializers = get_initializers() | ||
|
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| build_test_save( | ||
| name=name, | ||
| inputs=inputs, | ||
| outputs=outputs, | ||
| initializers=initializers, | ||
| attributes={"mode": "constant"}, | ||
| ) | ||
|
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| if __name__ == "__main__": | ||
| main() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,104 @@ | ||
| use std::str::FromStr; | ||
|
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| 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; | ||
|
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| #[derive(Config, Debug)] | ||
| pub struct PadConfig { | ||
| pub pads: Vec<usize>, | ||
| pub constant_value: f32, | ||
| } | ||
|
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| #[derive(Debug, Clone, new)] | ||
| pub struct PadNode { | ||
| pub input: TensorType, | ||
| pub output: TensorType, | ||
| pub config: PadConfig, | ||
| } | ||
|
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| 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; | ||
|
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| 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(); | ||
|
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| quote! { | ||
| let #output = #input.pad((#(#pads),*), #constant_value); | ||
| } | ||
| } | ||
| fn into_node(self) -> Node<PS> { | ||
| Node::Pad(self) | ||
| } | ||
|
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| fn register_imports(&self, imports: &mut crate::burn::BurnImports) { | ||
| imports.register("burn::tensor::ElementConversion"); | ||
| } | ||
| } | ||
|
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| #[cfg(test)] | ||
| mod tests { | ||
| use burn::record::FullPrecisionSettings; | ||
|
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| use super::*; | ||
| use crate::burn::{ | ||
| graph::BurnGraph, | ||
| node::{pad::PadNode, test::assert_tokens}, | ||
| TensorType, | ||
| }; | ||
|
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| #[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()]); | ||
|
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| let expected = quote! { | ||
| use burn::tensor::ElementConversion; | ||
| use burn::{ | ||
| module::Module, | ||
| tensor::{backend::Backend, Tensor}, | ||
| }; | ||
|
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| #[derive(Module, Debug)] | ||
| pub struct Model<B: Backend> { | ||
| 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 { | ||
| 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 | ||
| } | ||
| } | ||
| }; | ||
|
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| assert_tokens(graph.codegen(), expected); | ||
| } | ||
| } |
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