From 0291b8067260c086e64281c2338234693df91417 Mon Sep 17 00:00:00 2001
From: Duncan Tourolle <duncan@tourolle.paris>
Date: Sun, 4 Oct 2026 19:39:16 -0400
Subject: [PATCH] Feed every input in ep_probe

The denoiser takes `mosaic` and `sigma`; with only the first fed, every
provider reported the same failure and the model went unmeasured.
---
 core/dr-inference-engine/examples/ep_probe.rs | 32 ++++++++++++-------
 1 file changed, 21 insertions(+), 11 deletions(-)

diff --git a/core/dr-inference-engine/examples/ep_probe.rs b/core/dr-inference-engine/examples/ep_probe.rs
index bdb0e62..3b60757 100644
--- a/core/dr-inference-engine/examples/ep_probe.rs
+++ b/core/dr-inference-engine/examples/ep_probe.rs
@@ -181,19 +181,29 @@ fn migraphx(
 
 /// Median of `runs` timed runs over zeros, in milliseconds, after warm-ups.
 fn time(session: &mut ort::session::Session, warmups: usize, runs: usize) -> Result<f64, String> {
-    let shape: Vec<usize> = session.inputs()[0]
-        .dtype()
-        .tensor_shape()
-        .ok_or("input is not a tensor")?
-        .iter()
-        .map(|&d| if d > 0 { d as usize } else { 1 })
-        .collect();
-    let zeros = vec![0f32; shape.iter().product()];
+    // Zeros for every input, not just the first: the denoiser takes
+    // `mosaic` and `sigma`. A dynamic dimension is read as 1.
+    let mut inputs = Vec::new();
+    for input in session.inputs() {
+        let shape: Vec<usize> = input
+            .dtype()
+            .tensor_shape()
+            .ok_or("input is not a tensor")?
+            .iter()
+            .map(|&d| if d > 0 { d as usize } else { 1 })
+            .collect();
+        let zeros = vec![0f32; shape.iter().product()];
+        inputs.push((input.name().to_string(), shape, zeros));
+    }
     let once = |s: &mut ort::session::Session| -> Result<f64, String> {
-        let input = ort::value::Tensor::from_array((shape.clone(), zeros.clone()))
-            .map_err(|e| e.to_string())?;
+        let mut values = Vec::with_capacity(inputs.len());
+        for (name, shape, zeros) in &inputs {
+            let value = ort::value::Tensor::from_array((shape.clone(), zeros.clone()))
+                .map_err(|e| e.to_string())?;
+            values.push((name.clone(), ort::session::SessionInputValue::from(value)));
+        }
         let t = Instant::now();
-        let out = s.run(ort::inputs![input]).map_err(|e| e.to_string())?;
+        let out = s.run(values).map_err(|e| e.to_string())?;
         let _ = out[0]
             .try_extract_tensor::<f32>()
             .map_err(|e| e.to_string())?;