Adding more images to the demo (#46)

Now the demos will run the identification on 5 images.

assemblyscript/demo.sh

@@ -12,6 +12,7 @@ FIXTURE=https://github.com/intel/openvino-rs/raw/main/crates/openvino/tests/fixt
 wget --no-clobber --directory-prefix=$DOWNLOAD_DIR $FIXTURE/mobilenet.bin
 wget --no-clobber --directory-prefix=$DOWNLOAD_DIR $FIXTURE/mobilenet.xml
 wget --no-clobber --directory-prefix=$DOWNLOAD_DIR $FIXTURE/tensor-1x224x224x3-f32.bgr
+cp -rn images $DOWNLOAD_DIR
 
 # Run the demo
 wasmtime run build/optimized.wasm --dir build

assemblyscript/examples/object-classification.ts

@@ -8,24 +8,27 @@ import { IMAGENET_CLASSES } from "../assembly/imagenet_classes";
  */
 export function main(): i32 {
     Console.log("Loading graph...");
-
     const graph = Graph.load([readBytes("mobilenet.xml"), readBytes("mobilenet.bin")], GraphEncoding.openvino, ExecutionTarget.cpu);
 
     Console.log("Setting up execution context...");
     const context = graph.initExecutionContext();
-    const input = new Tensor([1, 3, 224, 224], TensorType.f32, readBytes("tensor-1x224x224x3-f32.bgr"));
-    context.setInput(0, input);
 
-    Console.log("Running classification...");
-    context.compute();
-    let maxBufferLength = 4004; // Size of our output buffer
-    const output = context.getOutput(0, new Array<u8>(4004).fill(0));
+    for (let i = 0; i < 5; i++) {
+        const input = new Tensor([1, 3, 224, 224], TensorType.f32, readBytes("images/" + i.toString() + ".bgr"));
+        context.setInput(0, input);
 
-    const results = sortResults(output, 5);
-    Console.log("Top 5 results: ");
-    // TODO figure out why we cannot use `forEach` here.
-    for (let i = 0; i < results.length; i++) {
-        Console.log((i + 1).toString() + ".) " + IMAGENET_CLASSES[results[i].id] + " : (" + results[i].id.toString() + ", " + results[i].probability.toString() + ")");
+        Console.log("Running classification...");
+        context.compute();
+        let maxBufferLength = 4004; // Size of our output buffer
+        const output = context.getOutput(0, new Array<u8>(4004).fill(0));
+
+        const results = sortResults(output, 5);
+        Console.log("Top 5 results: ");
+
+        // TODO figure out why we cannot use `forEach` here.
+        for (let i = 0; i < results.length; i++) {
+            Console.log((i + 1).toString() + ".) " + IMAGENET_CLASSES[results[i].id] + " : (" + results[i].id.toString() + ", " + results[i].probability.toString() + ")");
+        }
     }
     return 0;
 }
@@ -76,10 +79,10 @@ class Result {
  * https://github.com/jedisct1/as-wasi/blob/master/assembly/as-wasi.ts#L1100); that function should
  * be exported in as-wasi's `index` (TODO) to make it accessible using `--use
  * abort=as-wasi/wasi_abort` (see https://www.assemblyscript.org/debugging.html#overriding-abort).
- * @param message 
- * @param fileName 
- * @param lineNumber 
- * @param columnNumber 
+ * @param message
+ * @param fileName
+ * @param lineNumber
+ * @param columnNumber
  */
 export function wasi_abort(
     message: string = "",

build.sh

@@ -23,6 +23,7 @@ else
             cargo build --release --target=wasm32-wasi
             mkdir -p $WASI_NN_DIR/rust/examples/classification-example/build
             RUST_BUILD_DIR=$(realpath $WASI_NN_DIR/rust/examples/classification-example/build/)
+            cp -rn images $RUST_BUILD_DIR
             pushd examples/classification-example
             cargo build --release --target=wasm32-wasi
             cp target/wasm32-wasi/release/wasi-nn-example.wasm $RUST_BUILD_DIR

rust/examples/classification-example/src/main.rs

@@ -25,45 +25,48 @@ pub fn main() {
 
     // Load a tensor that precisely matches the graph input tensor (see
     // `fixture/frozen_inference_graph.xml`).
-    let tensor_data = fs::read("fixture/tensor-1x224x224x3-f32.bgr").unwrap();
-    println!("Read input tensor, size in bytes: {}", tensor_data.len());
-    let tensor = wasi_nn::Tensor {
-        dimensions: &[1, 3, 224, 224],
-        r#type: wasi_nn::TENSOR_TYPE_F32,
-        data: &tensor_data,
-    };
-    unsafe {
-        wasi_nn::set_input(context, 0, tensor).unwrap();
-    }
+    for i in 0..4 {
+        let filename: String = format!("{}{}{}", "fixture/images/", i, ".bgr");
+        let tensor_data = fs::read(filename).unwrap();
+        println!("Read input tensor, size in bytes: {}", tensor_data.len());
+        let tensor = wasi_nn::Tensor {
+            dimensions: &[1, 3, 224, 224],
+            r#type: wasi_nn::TENSOR_TYPE_F32,
+            data: &tensor_data,
+        };
+        unsafe {
+            wasi_nn::set_input(context, 0, tensor).unwrap();
+        }
 
-    // Execute the inference.
-    unsafe {
-        wasi_nn::compute(context).unwrap();
-    }
-    println!("Executed graph inference");
+        // Execute the inference.
+        unsafe {
+            wasi_nn::compute(context).unwrap();
+        }
+        println!("Executed graph inference");
 
-    // Retrieve the output.
-    let mut output_buffer = vec![0f32; 1001];
-    unsafe {
-        wasi_nn::get_output(
-            context,
-            0,
-            &mut output_buffer[..] as *mut [f32] as *mut u8,
-            (output_buffer.len() * 4).try_into().unwrap(),
-        )
-        .unwrap();
-    }
-    
-    let results = sort_results(&output_buffer);
-    println!(
-        "Found results, sorted top 5: {:?}",
-        &results[..5]
-    );
+        // Retrieve the output.
+        let mut output_buffer = vec![0f32; 1001];
+        unsafe {
+            wasi_nn::get_output(
+                context,
+                0,
+                &mut output_buffer[..] as *mut [f32] as *mut u8,
+                (output_buffer.len() * 4).try_into().unwrap(),
+            )
+            .unwrap();
+        }
+
+        let results = sort_results(&output_buffer);
+        println!(
+            "Found results, sorted top 5: {:?}",
+            &results[..5]
+        );
 
-    for i in 0..5 {
-        println!("{}.) {}", i + 1, imagenet_classes::IMAGENET_CLASSES[results[i].0]);
+        for i in 0..5 {
+            println!("{}.) {}", i + 1, imagenet_classes::IMAGENET_CLASSES[results[i].0]);
+        }
     }
-    
+
 }
 
 // Sort the buffer of probabilities. The graph places the match probability for each class at the