wasi-nn: add "named model" test

Add a new example crate which loads a model by name and performs image
classification. It uses the same MobileNet model as the existing test
but a new version of the Rust bindings. The new crate is built and run
with the new CLI flag in the `ci/run-wasi-nn-example.sh` script.

prtest:full

ci/run-wasi-nn-example.sh

@@ -1,10 +1,11 @@
 #!/bin/bash
 
-# The following script demonstrates how to execute a machine learning inference using the wasi-nn
-# module optionally compiled into Wasmtime. Calling it will download the necessary model and tensor
-# files stored separately in $FIXTURE into $TMP_DIR (optionally pass a directory with existing files
-# as the first argument to re-try the script). Then, it will compile the example code in
-# crates/wasi-nn/tests/example into a Wasm file that is subsequently executed with the Wasmtime CLI.
+# The following script demonstrates how to execute a machine learning inference
+# using the wasi-nn module optionally compiled into Wasmtime. Calling it will
+# download the necessary model and tensor files stored separately in $FIXTURE
+# into $TMP_DIR (optionally pass a directory with existing files as the first
+# argument to re-try the script). Then, it will compile and run several examples
+# in the Wasmtime CLI.
 set -e
 WASMTIME_DIR=$(dirname "$0" | xargs dirname)
 FIXTURE=https://github.com/intel/openvino-rs/raw/main/crates/openvino/tests/fixtures/mobilenet
@@ -18,24 +19,40 @@ else
     REMOVE_TMP_DIR=0
 fi
 
-# Build Wasmtime with wasi-nn enabled; we attempt this first to avoid extra work if the build fails.
+# One of the examples expects to be in a specifically-named directory.
+mkdir -p $TMP_DIR/mobilenet
+TMP_DIR=$TMP_DIR/mobilenet
+
+# Build Wasmtime with wasi-nn enabled; we attempt this first to avoid extra work
+# if the build fails.
 cargo build -p wasmtime-cli --features wasi-nn
 
 # Download all necessary test fixtures to the temporary directory.
 wget --no-clobber $FIXTURE/mobilenet.bin --output-document=$TMP_DIR/model.bin
 wget --no-clobber $FIXTURE/mobilenet.xml --output-document=$TMP_DIR/model.xml
 wget --no-clobber $FIXTURE/tensor-1x224x224x3-f32.bgr --output-document=$TMP_DIR/tensor.bgr
 
-# Now build an example that uses the wasi-nn API.
+# Now build an example that uses the wasi-nn API. Run the example in Wasmtime
+# (note that the example uses `fixture` as the expected location of the
+# model/tensor files).
 pushd $WASMTIME_DIR/crates/wasi-nn/examples/classification-example
 cargo build --release --target=wasm32-wasi
 cp target/wasm32-wasi/release/wasi-nn-example.wasm $TMP_DIR
 popd
+cargo run -- run --mapdir fixture::$TMP_DIR \
+    --wasi-modules=experimental-wasi-nn $TMP_DIR/wasi-nn-example.wasm
 
-# Run the example in Wasmtime (note that the example uses `fixture` as the expected location of the model/tensor files).
-cargo run -- run --mapdir fixture::$TMP_DIR --wasi-modules=experimental-wasi-nn $TMP_DIR/wasi-nn-example.wasm
+# Build and run another example, this time using Wasmtime's --graph flag to
+# preload the model.
+pushd $WASMTIME_DIR/crates/wasi-nn/examples/classification-example-named
+cargo build --release --target=wasm32-wasi
+cp target/wasm32-wasi/release/wasi-nn-example-named.wasm $TMP_DIR
+popd
+cargo run -- run --mapdir fixture::$TMP_DIR --graph openvino::$TMP_DIR \
+    --wasi-modules=experimental-wasi-nn $TMP_DIR/wasi-nn-example-named.wasm
 
-# Clean up the temporary directory only if it was not specified (users may want to keep the directory around).
+# Clean up the temporary directory only if it was not specified (users may want
+# to keep the directory around).
 if [[ $REMOVE_TMP_DIR -eq 1 ]]; then
     rm -rf $TMP_DIR
 fi

crates/wasi-nn/examples/classification-example-named/Cargo.toml

@@ -0,0 +1,15 @@
+[package]
+name = "wasi-nn-example-named"
+version = "0.0.0"
+authors = ["The Wasmtime Project Developers"]
+readme = "README.md"
+edition = "2021"
+publish = false
+
+[dependencies]
+wasi-nn = "0.5.0"
+
+# This crate is built with the wasm32-wasi target, so it's separate
+# from the main Wasmtime build, so use this directive to exclude it
+# from the parent directory's workspace.
+[workspace]

crates/wasi-nn/examples/classification-example-named/README.md

@@ -0,0 +1,2 @@
+This example project demonstrates using the `wasi-nn` API to perform ML inference. It consists of Rust code that is 
+built using the `wasm32-wasi` target. See `ci/run-wasi-nn-example.sh` for how this is used.

crates/wasi-nn/examples/classification-example-named/src/main.rs

@@ -0,0 +1,53 @@
+use std::fs;
+use wasi_nn::*;
+
+pub fn main() {
+    let graph = GraphBuilder::new(GraphEncoding::Openvino, ExecutionTarget::CPU)
+        .build_from_cache("mobilenet")
+        .unwrap();
+    println!("Loaded a graph: {:?}", graph);
+
+    let mut context = graph.init_execution_context().unwrap();
+    println!("Created an execution context: {:?}", context);
+
+    // Load a tensor that precisely matches the graph input tensor (see
+    // `fixture/frozen_inference_graph.xml`).
+    let tensor_data = fs::read("fixture/tensor.bgr").unwrap();
+    println!("Read input tensor, size in bytes: {}", tensor_data.len());
+    context
+        .set_input(0, TensorType::F32, &[1, 3, 224, 224], &tensor_data)
+        .unwrap();
+
+    // Execute the inference.
+    context.compute().unwrap();
+    println!("Executed graph inference");
+
+    // Retrieve the output.
+    let mut output_buffer = vec![0f32; 1001];
+    context.get_output(0, &mut output_buffer[..]).unwrap();
+
+    println!(
+        "Found results, sorted top 5: {:?}",
+        &sort_results(&output_buffer)[..5]
+    )
+}
+
+// Sort the buffer of probabilities. The graph places the match probability for each class at the
+// index for that class (e.g. the probability of class 42 is placed at buffer[42]). Here we convert
+// to a wrapping InferenceResult and sort the results. It is unclear why the MobileNet output
+// indices are "off by one" but the `.skip(1)` below seems necessary to get results that make sense
+// (e.g. 763 = "revolver" vs 762 = "restaurant")
+fn sort_results(buffer: &[f32]) -> Vec<InferenceResult> {
+    let mut results: Vec<InferenceResult> = buffer
+        .iter()
+        .skip(1)
+        .enumerate()
+        .map(|(c, p)| InferenceResult(c, *p))
+        .collect();
+    results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
+    results
+}
+
+// A wrapper for class ID and match probabilities.
+#[derive(Debug, PartialEq)]
+struct InferenceResult(usize, f32);