fix: update the openvino exmaple

openvino-mobilenet-image/README.md

@@ -57,7 +57,7 @@ Please refer to [WasmEdge Docs](https://wasmedge.org/docs/contribute/source/plug
   cargo build --target wasm32-wasip1 --release
   cd ..
 
-  wasmedge --dir .:. ./rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet.wasm mobilenet.xml mobilenet.bin input.jpg
+  wasmedge --dir .:. ./rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet-image.wasm mobilenet.xml mobilenet.bin input.jpg
   ```
 
   If the commands above run successfully, you will get the output:

openvino-mobilenet-raw/README.md

@@ -57,7 +57,7 @@ Please refer to [WasmEdge Docs](https://wasmedge.org/docs/contribute/source/plug
   cargo build --target wasm32-wasip1 --release
   cd ..
 
-  wasmedge --dir .:. ./rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet-image.wasm mobilenet.xml mobilenet.bin input.jpg
+  wasmedge --dir .:. ./rust/target/wasm32-wasip1/release/wasmedge-wasinn-example-mobilenet.wasm mobilenet.xml mobilenet.bin tensor-1x224x224x3-f32.bgr
   ```
 
   If the commands above run successfully, you will get the output:

openvino-mobilenet-raw/rust/src/main.rs

@@ -22,10 +22,36 @@ pub fn main() -> Result<(), Box<dyn std::error::Error>> {
 
     // Load a tensor that precisely matches the graph input tensor (see
     // `fixture/frozen_inference_graph.xml`).
-    print!("Set input tensor ...");
+    print!("Load input tensor ...");
     let input_dims = vec![1, 3, 224, 224];
     let tensor_data = fs::read(tensor_name).unwrap();
-    context.set_input(0, TensorType::F32, &input_dims, tensor_data)?;
+    println!("done");
+
+    print!("Transpose input tensor ...");
+    // Transpose from [height, width, 3] to [3, height, width]
+    // For the historical reasons, the input tensor is in the format of [height, width, channels],
+    // but the graph expects it in the format of [channels, height, width].
+    // The input tensor is 224x224x3, so we need to transpose it
+    // to 3x224x224.
+    let height = 224;
+    let width = 224;
+    println!("tensor_data.len() = {}", tensor_data.len());
+    let mut transposed: Vec<u8> = vec![0; tensor_data.len()];
+    for ch in 0..3 {
+        for y in 0..height {
+            for x in 0..width {
+                let loc = y * height + x;
+                for b in 0..4 {
+                    transposed[(ch * width * height + loc) * 4 + b as usize] =
+                        tensor_data[(loc * 3 + ch) * 4 + b as usize];
+                }
+            }
+        }
+    }
+    println!("done");
+
+    print!("Set input tensor ...");
+    context.set_input(0, TensorType::F32, &input_dims, transposed)?;
     println!("done");
 
     print!("Perform graph inference ...");

openvino-road-segmentation-adas/openvino-road-seg-adas/src/main.rs

@@ -77,13 +77,17 @@ fn image_to_tensor(path: String, height: u32, width: u32) -> Vec<u8> {
     let bytes_required = raw_u8_arr.len() * 4;
     let mut u8_f32_arr: Vec<u8> = vec![0; bytes_required];
 
-    for i in 0..raw_u8_arr.len() {
-        // Read the number as a f32 and break it into u8 bytes
-        let u8_f32: f32 = raw_u8_arr[i] as f32;
-        let u8_bytes = u8_f32.to_ne_bytes();
-
-        for j in 0..4 {
-            u8_f32_arr[(i * 4) + j] = u8_bytes[j];
+    // put in NCHW format
+    let block_size: usize = (height * width) as usize;
+    for c in 0..3 {
+        for i in 0..block_size {
+            // Read the number as a f32 and break it into u8 bytes
+            let u8_f32: f32 = raw_u8_arr[i * 3 + c] as f32;
+            let u8_bytes = u8_f32.to_ne_bytes();
+
+            for j in 0..4 {
+                u8_f32_arr[(c * block_size + i) * 4 + j] = u8_bytes[j];
+            }
         }
     }
     return u8_f32_arr;