Check in sample showcasing Rust projection of Winrt (#342)

* Init rust squeezenet project

* Add winrt-rs submodule

* update .gitmodules

* Add squeezenet code

* More efficient sorting

* refactoring

* Make pretty

* add comments

* Add README.md

* nit

* Use macro to clean up code

* actually use winml nuget 1.4.0

* PR comments

* Update to latest winrt-rs master

Co-authored-by: Ryan Lai <[email protected]>

.gitmodules

@@ -1,3 +1,6 @@
 [submodule "Tools/WinML-Dashboard/deps/Netron"]
 	path = Tools/WinMLDashboard/deps/Netron
 	url = https://github.com/lutzroeder/Netron.git
+[submodule "Samples/RustSqueezenet/winrt-rs"]
+	path = Samples/RustSqueezenet/winrt-rs
+	url = https://github.com/microsoft/winrt-rs.git

Samples/RustSqueezenet/Cargo.toml

@@ -0,0 +1,21 @@
+[package]
+name = "rust_squeezenet"
+version = "0.1.0"
+authors = ["Microsoft"]
+edition = "2018"
+
+[build]
+target-dir = "target"
+
+[dependencies]
+winrt = { path = "./winrt-rs" }
+# NOTE: winrt_macros is needed as a dependency because Rust 1.46 is needed and hasn't been released yet.
+winrt_macros = { git = "https://github.com/microsoft/winrt-rs",  version = "0.7.2" }
+
+[build-dependencies]
+winrt = { path = "./winrt-rs" }
+
+# Nuget packages
+[package.metadata.winrt.dependencies]
+"Microsoft.Windows.SDK.Contracts" = "10.0.19041.1"
+"Microsoft.AI.MachineLearning" = "1.4.0"

Samples/RustSqueezenet/README.md

@@ -0,0 +1,34 @@
+# SqueezeNet Rust sample
+This is a desktop application that uses SqueezeNet, a pre-trained machine learning model, to detect the predominant object in an image selected by the user from a file.
+
+Note: SqueezeNet was trained to work with image sizes of 224x224, so you must provide an image of size 224X224.
+
+## Prerequisites
+- [Install Rustup](https://www.rust-lang.org/tools/install)
+- Install cargo-winrt through command prompt. Until Rust 1.46 is released, cargo-winrt should be installed through the winrt-rs git repository.
+  - ```cargo install --git https://github.com/microsoft/winrt-rs cargo-winrt```
+
+## Build and Run the sample
+1. This project requires Rust 1.46, which is currently in Beta. Rust release dates can be found [here](https://forge.rust-lang.org/). Rust Beta features can be enabled by running the following commands through command prompt in this current project directory after installation of Rustup :
+    - ``` rustup install beta ```
+    - ``` rustup override set beta ```
+2. Install the WinRT nuget dependencies with this command: ``` cargo winrt install ```
+3. Build the project by running ```cargo build``` for debug and ```cargo build --release``` for release.
+4. Run the sample by running this command through the command prompt. ``` cargo winrt run ```
+    - Another option would be to run the executable directly. Should be ```<git enlistment>\Samples\RustSqueezeNet\target\debug\rust_squeezenet.exe```
+
+## Sample output
+```
+C:\Repos\Windows-Machine-Learning\Samples\RustSqueezeNet> cargo winrt run
+    Finished installing WinRT dependencies in 0.47s
+    Finished dev [unoptimized + debuginfo] target(s) in 0.12s
+     Running `target\debug\rust_squeezenet.exe`
+Loading model C:\Repos\Windows-Machine-Learning\RustSqueezeNet\target\debug\Squeezenet.onnx
+Creating session
+Loading image file C:\Repos\Windows-Machine-Learning\RustSqueezeNet\target\debug\kitten_224.png
+Evaluating
+Results:
+  tabby tabby cat 0.9314611
+  Egyptian cat 0.06530659
+  tiger cat 0.0029267797
+```

Samples/RustSqueezenet/build.rs

@@ -0,0 +1,35 @@
+macro_rules! copy_file {
+    ($file:expr, $destination:expr) => {
+        match fs::copy($file,
+        $destination) {
+            Ok(file) => file,
+            Err(error) => panic!("Problem copying the file {} to {}: {:?}", $file, $destination, error),
+        };
+    }
+}
+
+fn copy_resources() {
+    use std::fs;
+    let profile = std::env::var("PROFILE").unwrap();
+    if profile == "debug" {
+        copy_file!("..\\..\\SharedContent\\media\\fish.png",".\\target\\debug\\fish.png");
+        copy_file!("..\\..\\SharedContent\\media\\fish.png",".\\target\\debug\\kitten_224.png");
+        copy_file!("..\\..\\SharedContent\\models\\SqueezeNet.onnx",".\\target\\debug\\SqueezeNet.onnx");
+        copy_file!("..\\SqueezeNetObjectDetection\\Desktop\\cpp\\Labels.txt",".\\target\\debug\\Labels.txt");
+    }
+    else if profile == "release" {
+        copy_file!("..\\..\\SharedContent\\media\\fish.png",".\\target\\release\\fish.png");
+        copy_file!("..\\..\\SharedContent\\media\\fish.png",".\\target\\release\\kitten_224.png");
+        copy_file!("..\\..\\SharedContent\\models\\SqueezeNet.onnx",".\\target\\release\\SqueezeNet.onnx");
+        copy_file!("..\\SqueezeNetObjectDetection\\Desktop\\cpp\\Labels.txt",".\\target\\release\\Labels.txt");
+    }
+}
+
+fn main() {
+    winrt::build!(
+        types
+            microsoft::ai::machine_learning::*
+            windows::graphics::imaging::*
+    );
+    copy_resources();
+}

Samples/RustSqueezenet/src/main.rs

@@ -0,0 +1,102 @@
+include!(concat!(env!("OUT_DIR"), "/winrt.rs"));
+
+macro_rules! handle_io_error_as_winrt_error {
+    ($expression:expr, $error_message:expr) => {
+        match $expression {
+            Ok(val) => val,
+            Err(_err) => return Err(winrt::Error::new(winrt::ErrorCode(Error::last_os_error().raw_os_error().unwrap() as u32), $error_message)),
+        }
+    }
+}
+
+fn main() -> winrt::Result<()> {
+    use microsoft::ai::machine_learning::*;
+    use winrt::ComInterface;
+
+    let model_path = get_current_dir()? + "\\Squeezenet.onnx";
+    println!("Loading model {}", model_path);
+    let learning_model = LearningModel::load_from_file_path(model_path)?;
+
+    let device = LearningModelDevice::create(LearningModelDeviceKind::Cpu)?;
+
+    println!("Creating session");
+    let session = LearningModelSession::create_from_model_on_device(learning_model, device)?;
+
+    let image_file_path = get_current_dir()? + "\\kitten_224.png";
+    println!("Loading image file {}", image_file_path);
+    let input_image_videoframe = load_image_file(image_file_path)?;
+    let input_image_feature_value = ImageFeatureValue::create_from_video_frame(input_image_videoframe)?;
+    let binding = LearningModelBinding::create_from_session(&session)?;
+    binding.bind("data_0", input_image_feature_value)?;
+
+    println!("Evaluating");
+    let results = LearningModelSession::evaluate(&session,binding, "RunId")?;
+
+    let result_lookup = results.outputs()?.lookup("softmaxout_1")?;
+    let result_itensor_float : ITensorFloat = result_lookup.try_query()?;
+    let result_vector_view = result_itensor_float.get_as_vector_view()?;
+    println!("Results:");
+    print_results(result_vector_view)?;
+    Ok(())
+}
+
+// Print the evaluation results.
+fn print_results(results: windows::foundation::collections::IVectorView<f32>) -> winrt::Result<()> {
+    let labels = load_labels()?;
+    let mut sorted_results : std::vec::Vec<(f32,u32)> = Vec::new();
+    for i in 0..results.size()? {
+        let result = (results.get_at(i)?, i);
+        sorted_results.push(result);
+    }
+    sorted_results.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap());
+
+    // Display the top results
+    for i in 0..3 {
+        println!("  {} {}", labels[sorted_results[i].1 as usize], sorted_results[i].0)
+    }
+    Ok(())
+}
+
+// Return the path of the current directory of the executable
+fn get_current_dir() -> winrt::Result<String> {
+    use std::env;
+    use std::io::Error;
+    let current_exe = handle_io_error_as_winrt_error!(env::current_exe(), "Failed to get current directory of executable.");
+    let current_dir = current_exe.parent().unwrap();
+    Ok(current_dir.display().to_string())
+}
+
+// Load all the SqueezeNet labeels and return in a vector of Strings.
+fn load_labels() -> winrt::Result<std::vec::Vec<String>> {
+    use std::io::Error;
+    use std::fs::File;
+    use std::io::{prelude::*, BufReader};
+
+    let mut labels : std::vec::Vec<String> = Vec::new();
+    let labels_file_path = get_current_dir()? + "\\Labels.txt";
+    let file = handle_io_error_as_winrt_error!(File::open(labels_file_path), "Failed to load labels.");
+    let reader = BufReader::new(file);
+    for line in reader.lines() {
+        let line_str = handle_io_error_as_winrt_error!(line,"Failed to read lines.");
+        let mut tokenized_line: Vec<&str> = line_str.split(',').collect();
+        let index = tokenized_line[0].parse::<usize>().unwrap();
+        labels.resize(index+1, "".to_string());
+        tokenized_line.remove(0);
+        labels[index] = tokenized_line.join("");
+    }
+    Ok(labels)
+}
+
+// load image file given a path and return Videoframe
+fn load_image_file(image_file_path: String) -> winrt::Result<windows::media::VideoFrame> {
+    use windows::graphics::imaging::*;
+    use windows::media::*;
+    use windows::storage::*;
+
+    let file = StorageFile::get_file_from_path_async(image_file_path)?.get()?;
+    let stream = file.open_async(FileAccessMode::Read)?.get()?;
+    let decoder = BitmapDecoder::create_async(&stream)?.get()?;
+    let software_bitmap = decoder.get_software_bitmap_async()?.get()?;
+    let image_videoframe = VideoFrame::create_with_software_bitmap(software_bitmap)?;
+    Ok(image_videoframe)
+}