remove model files, download them instead. Switch from resnet18 to sq…

…ueezenet1.1

crates/wasi-nn/Cargo.toml

@@ -75,4 +75,4 @@ pytorch = ["dep:tch"]
 
 [[test]]
 name = "test-programs"
-harness = false
+harness = false

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

@@ -7,8 +7,12 @@ your system.
     - `export LIBTORCH=/path/to/libtorch`
 2. Build Wasmtime  with `wasmtime-wasi-nn/pytorch` feature.
 3. Navigate to this example directory `crates/wasi-nn/examples/classification-example-pytorch`.
+4. Download `squeezenet1_1.pt` model 
+```
+curl https://github.com/rahulchaphalkar/libtorch-models/releases/download/v0.1/squeezenet1_1.pt --output fixture/model.pt -L
+```
 4. Build this example `cargo build --target=wasm32-wasip1`.
-5. Run the generated wasm file with wasmtime after mapping the directory containing Resnet18 `model.pt` and sample image `kitten.png`
+5. Run the generated wasm file with wasmtime after mapping the directory containing squeezenet1.1 `model.pt` and sample image `kitten.png`
     ```
     ${Wasmtime_root_dir}/target/debug/wasmtime -S nn --dir ${Wasmtime_root_dir}/crates/wasi-nn/examples/classification-example-pytorch::. ${Wasmtime_root_dir}/crates/wasi-nn/examples/classification-example-pytorch/target/wasm32-wasip1/debug/wasi-nn-example-pytorch.wasm
     ```

crates/wasi-nn/tests/check/pytorch.rs

@@ -1,29 +1,32 @@
-use super::{artifacts_dir, DOWNLOAD_LOCK};
-use anyhow::Result;
+use super::{artifacts_dir, download, DOWNLOAD_LOCK};
+use anyhow::{bail, Context, Result};
 use std::{env, fs};
 
+/// Return `Ok` if we find the cached MobileNet test artifacts; this will
+/// download the artifacts if necessary.
 pub fn are_artifacts_available() -> Result<()> {
     let _exclusively_retrieve_artifacts = DOWNLOAD_LOCK.lock().unwrap();
+    const PYTORCH_BASE_URL: &str =
+        "https://github.com/rahulchaphalkar/libtorch-models/releases/download/v0.1/squeezenet1_1.pt";
     let artifacts_dir = artifacts_dir();
     if !artifacts_dir.is_dir() {
         fs::create_dir(&artifacts_dir)?;
     }
 
-    // Copy preprocessed image tensor from source tree to artifact directory.
+    let local_path = artifacts_dir.join("model.pt");
+    let remote_url = PYTORCH_BASE_URL;
+    if !local_path.is_file() {
+        download(&remote_url, &local_path).with_context(|| "unable to retrieve test artifact")?;
+    } else {
+        println!("> using cached artifact: {}", local_path.display())
+    }
+
+    // Copy image from source tree to artifact directory.
     let image_path = env::current_dir()?
         .join("tests")
         .join("fixtures")
         .join("kitten.tensor");
     let dest_path = artifacts_dir.join("kitten.tensor");
     fs::copy(&image_path, &dest_path)?;
-
-    // Copy Resnet18 model from source tree to artifact directory.
-    let image_path = env::current_dir()?
-        .join("tests")
-        .join("fixtures")
-        .join("resnet.pt");
-    let dest_path = artifacts_dir.join("model.pt");
-    fs::copy(&image_path, &dest_path)?;
-
     Ok(())
 }