mirror of
https://github.com/ruvnet/RuView
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rUv
004a63e82d
- Upgrade openssl to 0.10.78 (CVE-2026-41676), jsonwebtoken to 9.4 - Suppress unmaintained-only/no-CVE advisories in .cargo/audit.toml with per-entry rationale - Fix all `cargo clippy --all-targets -- -D warnings` errors across 35 crates: derivable_impls, needless_range_loop, map_or→is_some_and/ is_none_or, await_holding_lock (drop MutexGuard before .await), ptr_arg (&mut Vec→&mut [T]), useless_conversion, approximate_constant (2.718→E, 3.14→PI), field_reassign_with_default, manual_inspect, useless_vec, lines_filter_map_ok, print_literal, dead_code - Apply `cargo fmt --all` - Pre-existing test failure in wifi-densepose-signal (test_estimate_occupancy_noise_only) is not introduced by this PR
113 lines
3.3 KiB
Rust
113 lines
3.3 KiB
Rust
//! Benchmarks for neural network inference.
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use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
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use wifi_densepose_nn::{inference::EngineBuilder, tensor::Tensor};
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fn bench_tensor_operations(c: &mut Criterion) {
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let mut group = c.benchmark_group("tensor_ops");
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for size in [32, 64, 128].iter() {
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let tensor = Tensor::zeros_4d([1, 256, *size, *size]);
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group.throughput(Throughput::Elements((size * size * 256) as u64));
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group.bench_with_input(BenchmarkId::new("relu", size), size, |b, _| {
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b.iter(|| black_box(tensor.relu().unwrap()))
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});
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group.bench_with_input(BenchmarkId::new("sigmoid", size), size, |b, _| {
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b.iter(|| black_box(tensor.sigmoid().unwrap()))
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});
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group.bench_with_input(BenchmarkId::new("tanh", size), size, |b, _| {
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b.iter(|| black_box(tensor.tanh().unwrap()))
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});
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}
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group.finish();
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}
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fn bench_densepose_inference(c: &mut Criterion) {
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let mut group = c.benchmark_group("densepose_inference");
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// Use MockBackend for benchmarking inference throughput
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let engine = EngineBuilder::new().build_mock();
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for size in [32, 64].iter() {
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let input = Tensor::zeros_4d([1, 256, *size, *size]);
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group.throughput(Throughput::Elements((size * size * 256) as u64));
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group.bench_with_input(BenchmarkId::new("inference", size), size, |b, _| {
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b.iter(|| black_box(engine.infer(&input).unwrap()))
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});
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}
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group.finish();
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}
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fn bench_translator_inference(c: &mut Criterion) {
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let mut group = c.benchmark_group("translator_inference");
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// Use MockBackend for benchmarking inference throughput
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let engine = EngineBuilder::new().build_mock();
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for size in [32, 64].iter() {
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let input = Tensor::zeros_4d([1, 128, *size, *size]);
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group.throughput(Throughput::Elements((size * size * 128) as u64));
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group.bench_with_input(BenchmarkId::new("inference", size), size, |b, _| {
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b.iter(|| black_box(engine.infer(&input).unwrap()))
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});
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}
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group.finish();
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}
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fn bench_mock_inference(c: &mut Criterion) {
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let mut group = c.benchmark_group("mock_inference");
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let engine = EngineBuilder::new().build_mock();
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let input = Tensor::zeros_4d([1, 256, 64, 64]);
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group.throughput(Throughput::Elements(1));
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group.bench_function("single_inference", |b| {
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b.iter(|| black_box(engine.infer(&input).unwrap()))
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});
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group.finish();
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}
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fn bench_batch_inference(c: &mut Criterion) {
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let mut group = c.benchmark_group("batch_inference");
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let engine = EngineBuilder::new().build_mock();
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for batch_size in [1, 2, 4, 8].iter() {
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let inputs: Vec<Tensor> = (0..*batch_size)
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.map(|_| Tensor::zeros_4d([1, 256, 64, 64]))
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.collect();
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group.throughput(Throughput::Elements(*batch_size as u64));
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group.bench_with_input(BenchmarkId::new("batch", batch_size), batch_size, |b, _| {
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b.iter(|| black_box(engine.infer_batch(&inputs).unwrap()))
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});
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}
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group.finish();
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}
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criterion_group!(
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benches,
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bench_tensor_operations,
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bench_densepose_inference,
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bench_translator_inference,
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bench_mock_inference,
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bench_batch_inference,
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);
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criterion_main!(benches);
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