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fix: adapt onnx example for ort rc.10 #232

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Merged
martinjrobins merged 1 commit into from
Jan 19, 2026
Merged

fix: adapt onnx example for ort rc.10 #232

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4 changes: 2 additions & 2 deletions Cargo.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -25,8 +25,8 @@ plotly = { version = "0.13.5" }
argmin = { version = "0.11.0" }
argmin-math = { version = "0.5.1" }
argmin-observer-slog = { version = "0.2.0" }
ort = "=2.0.0-rc.9"
ort-sys = { version = "=2.0.0-rc.9", default-features = false }
ort = "=2.0.0-rc.10"
ort-sys = { version = "=2.0.0-rc.10", default-features = false }

[profile.profiling]
inherits = "release"
Expand Down
2 changes: 1 addition & 1 deletion examples/neural-ode-weather-prediction/Cargo.toml
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Expand Up @@ -20,4 +20,4 @@ ort = { workspace = true, optional = true }
ort-sys = { workspace = true, optional = true }
ndarray = "0.16.1"
csv = "1.3.1"
rand = "0.9.0"
rand = "0.9.0"
127 changes: 66 additions & 61 deletions examples/neural-ode-weather-prediction/src/main.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@ use ndarray::Array1;
use ort::error::Result;
use ort::inputs;
use ort::session::{builder::GraphOptimizationLevel, Session};
use ort::value::{TensorRef, ValueType};
use plotly::common::{DashType, Line, Mode};
use plotly::layout::{Axis, GridPattern, LayoutGrid};
use plotly::{Layout, Plot, Scatter};
Expand All @@ -31,10 +32,10 @@ const BASE_DATA_DIR: &str = "examples/neural-ode-weather-prediction/src/data/";
const BASE_OUTPUT_DIR: &str = "examples/neural-ode-weather-prediction/";

struct NeuralOde {
rhs: Session,
rhs_jac_mul: Session,
rhs_jac_transpose_mul: Session,
rhs_sens_transpose_mul: Session,
rhs: RefCell<Session>,
rhs_jac_mul: RefCell<Session>,
rhs_jac_transpose_mul: RefCell<Session>,
rhs_sens_transpose_mul: RefCell<Session>,
input_y: RefCell<Array1<f32>>,
input_v: RefCell<Array1<f32>>,
input_p: Array1<f32>,
Expand All @@ -58,8 +59,17 @@ impl NeuralOde {
let mut nparams = 0;
for input in rhs.inputs.iter() {
if input.name == "p" {
nparams = input.input_type.tensor_dimensions().unwrap()[0] as usize;
break;
if let ValueType::Tensor { shape, .. } = &input.input_type {
let dim = shape
.get(0)
.copied()
.expect("p input should have at least one dimension");
if dim < 0 {
panic!("p input has dynamic dimension; cannot infer parameter count");
}
nparams = dim as usize;
break;
}
}
}
let mut rng = rand::rng();
Expand All @@ -69,10 +79,10 @@ impl NeuralOde {

Ok(Self {
y0,
rhs,
rhs_jac_mul,
rhs_jac_transpose_mul,
rhs_sens_transpose_mul,
rhs: RefCell::new(rhs),
rhs_jac_mul: RefCell::new(rhs_jac_mul),
rhs_jac_transpose_mul: RefCell::new(rhs_jac_transpose_mul),
rhs_sens_transpose_mul: RefCell::new(rhs_sens_transpose_mul),
input_p: params,
input_v: RefCell::new(y0_ndarray.clone()),
input_y: RefCell::new(y0_ndarray),
Expand Down Expand Up @@ -200,21 +210,19 @@ impl NonLinearOp for Rhs<'_> {
.iter_mut()
.zip(x.inner().iter())
.for_each(|(y, x)| *y = *x as f32);
let outputs = self
.0
.rhs
.run(
inputs![
"p" => self.0.input_p.view(),
"y" => y_input.view(),
]
.unwrap(),
)
let p_slice = self.0.input_p.as_slice().unwrap();
let y_slice = y_input.as_slice().unwrap();
let mut rhs = self.0.rhs.borrow_mut();
let outputs = rhs
.run(inputs![
"p" => TensorRef::from_array_view(([p_slice.len()], p_slice)).unwrap(),
"y" => TensorRef::from_array_view(([y_slice.len()], y_slice)).unwrap(),
])
.unwrap();
let y_data = outputs["Identity_1:0"].try_extract_tensor::<f32>().unwrap();
let (_shape, y_data) = outputs["Identity_1:0"].try_extract_tensor::<f32>().unwrap();
y.inner_mut()
.iter_mut()
.zip(y_data.as_slice().unwrap())
.zip(y_data.iter())
.for_each(|(y, x)| *y = *x as f64);
}
}
Expand All @@ -231,22 +239,21 @@ impl NonLinearOpJacobian for Rhs<'_> {
.iter_mut()
.zip(v.inner().iter())
.for_each(|(v, x)| *v = *x as f32);
let outputs = self
.0
.rhs_jac_mul
.run(
inputs![
"y" => y_input.view(),
"v" => v_input.view(),
"p" => self.0.input_p.view(),
]
.unwrap(),
)
let p_slice = self.0.input_p.as_slice().unwrap();
let y_slice = y_input.as_slice().unwrap();
let v_slice = v_input.as_slice().unwrap();
let mut rhs_jac_mul = self.0.rhs_jac_mul.borrow_mut();
let outputs = rhs_jac_mul
.run(inputs![
"y" => TensorRef::from_array_view(([y_slice.len()], y_slice)).unwrap(),
"v" => TensorRef::from_array_view(([v_slice.len()], v_slice)).unwrap(),
"p" => TensorRef::from_array_view(([p_slice.len()], p_slice)).unwrap(),
])
.unwrap();
let y_data = outputs["Identity_1:0"].try_extract_tensor::<f32>().unwrap();
let (_shape, y_data) = outputs["Identity_1:0"].try_extract_tensor::<f32>().unwrap();
y.inner_mut()
.iter_mut()
.zip(y_data.as_slice().unwrap())
.zip(y_data.iter())
.for_each(|(y, x)| *y = *x as f64);
}
}
Expand All @@ -263,22 +270,21 @@ impl NonLinearOpAdjoint for Rhs<'_> {
.iter_mut()
.zip(v.inner().iter())
.for_each(|(v, x)| *v = *x as f32);
let outputs = self
.0
.rhs_jac_transpose_mul
.run(
inputs![
"y" => y_input.view(),
"v" => v_input.view(),
"p" => self.0.input_p.view(),
]
.unwrap(),
)
let p_slice = self.0.input_p.as_slice().unwrap();
let y_slice = y_input.as_slice().unwrap();
let v_slice = v_input.as_slice().unwrap();
let mut rhs_jac_transpose_mul = self.0.rhs_jac_transpose_mul.borrow_mut();
let outputs = rhs_jac_transpose_mul
.run(inputs![
"y" => TensorRef::from_array_view(([y_slice.len()], y_slice)).unwrap(),
"v" => TensorRef::from_array_view(([v_slice.len()], v_slice)).unwrap(),
"p" => TensorRef::from_array_view(([p_slice.len()], p_slice)).unwrap(),
])
.unwrap();
let y_data = outputs["Identity_1:0"].try_extract_tensor::<f32>().unwrap();
let (_shape, y_data) = outputs["Identity_1:0"].try_extract_tensor::<f32>().unwrap();
y.inner_mut()
.iter_mut()
.zip(y_data.as_slice().unwrap())
.zip(y_data.iter())
.for_each(|(y, x)| *y = *x as f64);
}
}
Expand All @@ -295,22 +301,21 @@ impl NonLinearOpSensAdjoint for Rhs<'_> {
.iter_mut()
.zip(v.inner().iter())
.for_each(|(v, x)| *v = *x as f32);
let outputs = self
.0
.rhs_sens_transpose_mul
.run(
inputs![
"y" => y_input.view(),
"v" => v_input.view(),
"p" => self.0.input_p.view(),
]
.unwrap(),
)
let p_slice = self.0.input_p.as_slice().unwrap();
let y_slice = y_input.as_slice().unwrap();
let v_slice = v_input.as_slice().unwrap();
let mut rhs_sens_transpose_mul = self.0.rhs_sens_transpose_mul.borrow_mut();
let outputs = rhs_sens_transpose_mul
.run(inputs![
"y" => TensorRef::from_array_view(([y_slice.len()], y_slice)).unwrap(),
"v" => TensorRef::from_array_view(([v_slice.len()], v_slice)).unwrap(),
"p" => TensorRef::from_array_view(([p_slice.len()], p_slice)).unwrap(),
])
.unwrap();
let y_data = outputs["Identity_1:0"].try_extract_tensor::<f32>().unwrap();
let (_shape, y_data) = outputs["Identity_1:0"].try_extract_tensor::<f32>().unwrap();
y.inner_mut()
.iter_mut()
.zip(y_data.as_slice().unwrap())
.zip(y_data.iter())
.for_each(|(y, x)| *y = *x as f64);
}
}
Expand Down