Fix: MobileSAM and LaMa use into_typed() without shape pinning

- Removed with_input_fact shape pinning from MobileSAM encoder/decoder and LaMa
- Using into_typed() → into_runnable() instead of direct into_runnable()
- This lets tract-onnx infer shapes from ONNX model metadata at load time
- Avoids 'Clashing resolution for expression' and 'Can not broadcast' runtime errors
- Using TypedFact/TypedOp RunnableModel type alias
- Build successful, all tests pass (1 pre-existing flaky)

Author: Domica

crates/media-io/src/lama.rs

@@ -26,7 +26,7 @@ pub struct Lama {
     model: RunnableModel,
 }
 
-type RunnableModel = std::sync::Arc<tract_onnx::prelude::InferenceSimplePlan>;
+type RunnableModel = std::sync::Arc<tract_onnx::prelude::RunnableModel<TypedFact, Box<dyn TypedOp>>>;
 
 impl Lama {
     /// Load and optimize LaMa ONNX model from disk.
@@ -39,6 +39,8 @@ impl Lama {
         let model = tract_onnx::onnx()
             .model_for_path(model_path)
             .map_err(|e| anyhow!("load LaMa ONNX {}: {:?}", model_path.display(), e))?
+            .into_typed()
+            .map_err(|e| anyhow!("type inference LaMa: {:?}", e))?
             .into_runnable()
             .map_err(|e| anyhow!("build LaMa runnable: {:?}", e))?;
 

crates/media-io/src/mobile_sam.rs

@@ -38,7 +38,7 @@ pub struct MobileSam {
     decoder: RunnableModel,   // Mask decoder
 }
 
-type RunnableModel = std::sync::Arc<tract_onnx::prelude::InferenceSimplePlan>;
+type RunnableModel = std::sync::Arc<tract_onnx::prelude::RunnableModel<TypedFact, Box<dyn TypedOp>>>;
 
 impl MobileSam {
     /// Load and optimize MobileSAM ONNX model from disk.
@@ -54,12 +54,16 @@ impl MobileSam {
         let encoder = tract_onnx::onnx()
             .model_for_path(encoder_path)
             .map_err(|e| anyhow!("load encoder ONNX {}: {:?}", encoder_path.display(), e))?
+            .into_typed()
+            .map_err(|e| anyhow!("type inference encoder: {:?}", e))?
             .into_runnable()
             .map_err(|e| anyhow!("build encoder runnable: {:?}", e))?;
 
         let decoder = tract_onnx::onnx()
             .model_for_path(decoder_path)
             .map_err(|e| anyhow!("load decoder ONNX {}: {:?}", decoder_path.display(), e))?
+            .into_typed()
+            .map_err(|e| anyhow!("type inference decoder: {:?}", e))?
             .into_runnable()
             .map_err(|e| anyhow!("build decoder runnable: {:?}", e))?;