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Add a benchmark for wasi-nn ONNX models.
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benchmarks/image-classification/rust-benchmark/Cargo.lock
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benchmarks/image-classification/rust-benchmark/src/onnx.rs
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,55 @@ | ||
| use image::io::Reader; | ||
| use image::DynamicImage; | ||
| use sightglass_api as bench; | ||
| use std::convert::TryInto; | ||
| use std::fs; | ||
| use wasi_nn; | ||
| mod imagenet_classes; | ||
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| pub fn main() { | ||
| // Convert image to tensor data. | ||
| let tensor_data = fs::read("./kitten.rgb").unwrap(); | ||
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| // Load model from a file. | ||
| let graph = | ||
| wasi_nn::GraphBuilder::new(wasi_nn::GraphEncoding::Onnx, wasi_nn::ExecutionTarget::CPU) | ||
| .build_from_files(["./mobilenet.onnx"]) | ||
| .unwrap(); | ||
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| let mut context = graph.init_execution_context().unwrap(); | ||
| context | ||
| .set_input(0, wasi_nn::TensorType::F32, &[1, 3, 224, 224], &tensor_data) | ||
| .unwrap(); | ||
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| bench::start(); | ||
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| // Execute the inference. | ||
| context.compute().unwrap(); | ||
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| bench::end(); | ||
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| // Retrieve the output. | ||
| let mut output_buffer = vec![0f32; 1000]; | ||
| context.get_output(0, &mut output_buffer[..]).unwrap(); | ||
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| let result = sort_results(&output_buffer); | ||
| } | ||
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| // 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. | ||
| 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 | ||
| } | ||
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| // A wrapper for class ID and match probabilities. | ||
| #[derive(Debug, PartialEq)] | ||
| struct InferenceResult(usize, f32); |
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