chore: upgrade ort

Cargo.toml

@@ -62,11 +62,12 @@ features = [
   "bevy_winit",
   "multi-threaded",
   "png",
+  "tonemapping_luts",
 ]
 
 
 [dependencies.ort]
-version = "2.0.0-rc.0"
+version = "2.0.0-rc.2"
 default-features = false
 features = [
   "cuda",

benches/modnet.rs

@@ -86,7 +86,7 @@ fn modnet_output_to_luma_images_benchmark(c: &mut Criterion) {
 
     let session = Session::builder().unwrap()
         .with_optimization_level(GraphOptimizationLevel::Level3).unwrap()
-        .with_model_from_file("assets/modnet_photographic_portrait_matting.onnx").unwrap();
+        .commit_from_file("assets/modnet_photographic_portrait_matting.onnx").unwrap();
 
     let data = vec![0u8; (1920 * 1080 * 4) as usize];
     let image: Image = Image::new(
@@ -123,7 +123,7 @@ fn modnet_inference_benchmark(c: &mut Criterion) {
 
     let session = Session::builder().unwrap()
         .with_optimization_level(GraphOptimizationLevel::Level3).unwrap()
-        .with_model_from_file("assets/modnet_photographic_portrait_matting.onnx").unwrap();
+        .commit_from_file("assets/modnet_photographic_portrait_matting.onnx").unwrap();
 
     MAX_RESOLUTIONS.iter().for_each(|(width, height)| {
         let data = vec![0u8; *width as usize * *height as usize * 4];

benches/yolo_v8.rs

@@ -80,7 +80,7 @@ fn process_output_benchmark(c: &mut Criterion) {
 
     let session = Session::builder().unwrap()
         .with_optimization_level(GraphOptimizationLevel::Level3).unwrap()
-        .with_model_from_file("assets/yolov8n.onnx").unwrap();
+        .commit_from_file("assets/yolov8n.onnx").unwrap();
 
     RESOLUTIONS.iter()
         .for_each(|(width, height)| {
@@ -117,7 +117,7 @@ fn inference_benchmark(c: &mut Criterion) {
 
     let session = Session::builder().unwrap()
         .with_optimization_level(GraphOptimizationLevel::Level3).unwrap()
-        .with_model_from_file("assets/yolov8n.onnx").unwrap();
+        .commit_from_file("assets/yolov8n.onnx").unwrap();
 
     RESOLUTIONS.iter().for_each(|(width, height)| {
         let data = vec![0u8; *width as usize * *height as usize * 4];

src/lib.rs

@@ -88,7 +88,7 @@ impl AssetLoader for OnnxLoader {
                     // TODO: add session configuration
                     let session = Session::builder()?
                         .with_optimization_level(GraphOptimizationLevel::Level3)?
-                        .with_model_from_memory(&bytes)?;
+                        .commit_from_memory(&bytes)?;
 
                     Ok(Onnx {
                         session: Arc::new(Mutex::new(Some(session))),

src/models/flame.rs

@@ -134,10 +134,10 @@ pub fn post_process(
     vertices: &ort::Value,
     landmarks: &ort::Value,
 ) -> FlameOutput {
-    let vertices_tensor = vertices.extract_tensor::<f32>().unwrap();
+    let vertices_tensor = vertices.try_extract_tensor::<f32>().unwrap();
     let vertices_view = vertices_tensor.view();  // [8, 5023, 3]
 
-    let landmarks_tensor = landmarks.extract_tensor::<f32>().unwrap();
+    let landmarks_tensor = landmarks.try_extract_tensor::<f32>().unwrap();
     let landmarks_view = landmarks_tensor.view();  // [8, 68, 3]
 
     let vertices = vertices_view.outer_iter()

src/models/lightglue.rs

@@ -100,13 +100,13 @@ pub fn post_process(
     kpts1: &ort::Value,
     matches: &ort::Value,
 ) -> Result<Vec<GluedPair>, &'static str> {
-    let kpts0_tensor = kpts0.extract_tensor::<i64>().unwrap();
+    let kpts0_tensor = kpts0.try_extract_tensor::<i64>().unwrap();
     let kpts0_view = kpts0_tensor.view();
 
-    let kpts1_tensor = kpts1.extract_tensor::<i64>().unwrap();
+    let kpts1_tensor = kpts1.try_extract_tensor::<i64>().unwrap();
     let kpts1_view = kpts1_tensor.view();
 
-    let matches = matches.extract_tensor::<i64>().unwrap();
+    let matches = matches.try_extract_tensor::<i64>().unwrap();
     let matches_view = matches.view();
 
     Ok(

src/models/modnet.rs

@@ -52,7 +52,7 @@ pub fn modnet_inference(
 pub fn modnet_output_to_luma_images(
     output_value: &ort::Value,
 ) -> Vec<Image> {
-    let tensor = output_value.extract_tensor::<f32>().unwrap();
+    let tensor = output_value.try_extract_tensor::<f32>().unwrap();
     let data = tensor.view();
 
     let shape = data.shape();

src/models/yolo_v8.rs

@@ -90,7 +90,7 @@ pub fn process_output(
 ) -> Vec<BoundingBox> {
     let mut boxes = Vec::new();
 
-    let tensor = output.extract_tensor::<f32>().unwrap();
+    let tensor = output.try_extract_tensor::<f32>().unwrap();
     let data = tensor.view().t().into_owned();
 
     for detection  in data.axis_iter(Axis(0)) {