Use tensor data as input for wasi-nn WinML backend test.

To avoid involving too many external dependencies, input image is
converted to tensor data offline.

crates/test-programs/Cargo.toml

@@ -19,5 +19,4 @@ getrandom = "0.2.9"
 futures = { workspace = true, default-features = false, features = ['alloc'] }
 url = { workspace = true }
 sha2 = "0.10.2"
-base64 = "0.21.0"
-image2tensor = "0.3.1"
+base64 = "0.21.0"

crates/test-programs/src/bin/nn_image_classification_winml.rs

@@ -1,4 +1,5 @@
 use anyhow::Result;
+use std::fs;
 use std::time::Instant;
 use wasi_nn::*;
 
@@ -13,16 +14,7 @@ pub fn main() -> Result<()> {
     println!("Created an execution context.");
 
     // Convert image to tensor data.
-    let mut tensor_data = image2tensor::convert_image_to_planar_tensor_bytes(
-        "fixture/kitten.png",
-        224,
-        224,
-        image2tensor::TensorType::F32,
-        image2tensor::ColorOrder::RGB,
-    )
-    .unwrap();
-    // The model requires values in the range of [0, 1].
-    scale(&mut tensor_data);
+    let tensor_data = fs::read("fixture/kitten.rgb")?;
     context
         .set_input(0, TensorType::F32, &[1, 3, 224, 224], &tensor_data)
         .unwrap();
@@ -40,9 +32,8 @@ pub fn main() -> Result<()> {
     context.get_output(0, &mut output_buffer[..]).unwrap();
 
     let result = sort_results(&output_buffer);
-    assert_eq!(result[0].0, 284);
-
     println!("Found results, sorted top 5: {:?}", &result[..5]);
+    assert_eq!(result[0].0, 284);
     Ok(())
 }
 
@@ -62,17 +53,6 @@ fn sort_results(buffer: &[f32]) -> Vec<InferenceResult> {
     results
 }
 
-// Convert values from [0, 255] to [0, 1].
-fn scale(buffer: &mut Vec<u8>) {
-    const F32_LEN: usize = 4;
-    for i in (0..buffer.len()).step_by(F32_LEN) {
-        let mut num = f32::from_ne_bytes(buffer[i..i + F32_LEN].try_into().unwrap());
-        num /= 225.0;
-        let num_vec = num.to_ne_bytes().to_vec();
-        buffer.splice(i..i + F32_LEN, num_vec);
-    }
-}
-
 // A wrapper for class ID and match probabilities.
 #[derive(Debug, PartialEq)]
 struct InferenceResult(usize, f32);

crates/wasi-nn/src/testing.rs

@@ -97,15 +97,20 @@ fn check_winml_artifacts_are_available() -> Result<()> {
         fs::create_dir(&artifacts_dir)?;
     }
     const MODEL_URL: &str="https://github.com/onnx/models/raw/5faef4c33eba0395177850e1e31c4a6a9e634c82/vision/classification/mobilenet/model/mobilenetv2-12.onnx";
-    const IMG_URL: &str="https://github.com/microsoft/Windows-Machine-Learning/blob/master/SharedContent/media/kitten_224.png?raw=true";
-    for (from, to) in [(MODEL_URL, "mobilenet.onnx"), (IMG_URL, "kitten.png")] {
+    for (from, to) in [(MODEL_URL, "mobilenet.onnx")] {
         let local_path = artifacts_dir.join(to);
         if !local_path.is_file() {
             download(&from, &local_path).with_context(|| "unable to retrieve test artifact")?;
         } else {
             println!("> using cached artifact: {}", local_path.display())
         }
     }
+    // kitten.rgb is converted from https://github.com/microsoft/Windows-Machine-Learning/blob/master/SharedContent/media/kitten_224.png?raw=true.
+    let tensor_path = env::current_dir()?
+        .join("tests")
+        .join("fixtures")
+        .join("kitten.rgb");
+    fs::copy(tensor_path, artifacts_dir.join("kitten.rgb"))?;
     Ok(())
 }