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The Rust port lived two directories deep (rust-port/wifi-densepose-rs/) without any sibling under rust-port/ that warranted the extra level. Move the whole workspace up to v2/ to match v1/ (Python) at the same depth and shorten every cd / build command across the repo. git mv preserves history for all tracked files. 60 files updated for path references (CI workflows, ADRs, docs, scripts, READMEs, internal .claude-flow state). Two manual fixes for relative-cd paths in CLAUDE.md and ADR-043 that became wrong after the depth change (cd ../.. → cd ..). Validated: - cargo check --workspace --no-default-features → clean (after target/ nuke; the gitignored target/ was carried by the OS rename and had hard-coded old paths in build scripts) - cargo test --workspace --no-default-features → 1,539 passed, 0 failed, 8 ignored (same totals as pre-rename) - ESP32-S3 on COM7 → still streaming live CSI (cb #40300, RSSI -64 dBm) After-merge follow-up: contributors should `rm -rf v2/target` once and let cargo regenerate from the new path.
72 lines
2.5 KiB
Rust
72 lines
2.5 KiB
Rust
//! # WiFi-DensePose Neural Network Crate
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//!
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//! This crate provides neural network inference capabilities for the WiFi-DensePose
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//! pose estimation system. It supports multiple backends including ONNX Runtime,
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//! tch-rs (PyTorch), and Candle for flexible deployment.
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//!
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//! ## Features
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//!
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//! - **DensePose Head**: Body part segmentation and UV coordinate regression
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//! - **Modality Translator**: CSI to visual feature space translation
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//! - **Multi-Backend Support**: ONNX, PyTorch (tch), and Candle backends
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//! - **Inference Optimization**: Batching, GPU acceleration, and model caching
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//!
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//! ## Example
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//!
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//! ```rust,ignore
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//! use wifi_densepose_nn::{InferenceEngine, DensePoseConfig, OnnxBackend};
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//!
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//! // Create inference engine with ONNX backend
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//! let config = DensePoseConfig::default();
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//! let backend = OnnxBackend::from_file("model.onnx")?;
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//! let engine = InferenceEngine::new(backend, config)?;
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//!
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//! // Run inference
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//! let input = ndarray::Array4::zeros((1, 256, 64, 64));
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//! let output = engine.infer(&input)?;
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//! ```
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#![warn(missing_docs)]
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#![warn(rustdoc::missing_doc_code_examples)]
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#![deny(unsafe_code)]
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pub mod densepose;
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pub mod error;
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pub mod inference;
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#[cfg(feature = "onnx")]
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pub mod onnx;
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pub mod tensor;
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pub mod translator;
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// Re-exports for convenience
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pub use densepose::{DensePoseConfig, DensePoseHead, DensePoseOutput};
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pub use error::{NnError, NnResult};
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pub use inference::{Backend, InferenceEngine, InferenceOptions};
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#[cfg(feature = "onnx")]
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pub use onnx::{OnnxBackend, OnnxSession};
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pub use tensor::{Tensor, TensorShape};
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pub use translator::{ModalityTranslator, TranslatorConfig, TranslatorOutput};
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/// Prelude module for convenient imports
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pub mod prelude {
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pub use crate::densepose::{DensePoseConfig, DensePoseHead, DensePoseOutput};
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pub use crate::error::{NnError, NnResult};
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pub use crate::inference::{Backend, InferenceEngine, InferenceOptions};
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#[cfg(feature = "onnx")]
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pub use crate::onnx::{OnnxBackend, OnnxSession};
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pub use crate::tensor::{Tensor, TensorShape};
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pub use crate::translator::{ModalityTranslator, TranslatorConfig, TranslatorOutput};
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}
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/// Version information
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pub const VERSION: &str = env!("CARGO_PKG_VERSION");
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/// Number of body parts in DensePose model (standard configuration)
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pub const NUM_BODY_PARTS: usize = 24;
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/// Number of UV coordinates (U and V)
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pub const NUM_UV_COORDINATES: usize = 2;
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/// Default hidden channel sizes for networks
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pub const DEFAULT_HIDDEN_CHANNELS: &[usize] = &[256, 128, 64];
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