6ed69a3d48
Major changes: - Organized Python v1 implementation into v1/ subdirectory - Created Rust workspace with 9 modular crates: - wifi-densepose-core: Core types, traits, errors - wifi-densepose-signal: CSI processing, phase sanitization, FFT - wifi-densepose-nn: Neural network inference (ONNX/Candle/tch) - wifi-densepose-api: Axum-based REST/WebSocket API - wifi-densepose-db: SQLx database layer - wifi-densepose-config: Configuration management - wifi-densepose-hardware: Hardware abstraction - wifi-densepose-wasm: WebAssembly bindings - wifi-densepose-cli: Command-line interface Documentation: - ADR-001: Workspace structure - ADR-002: Signal processing library selection - ADR-003: Neural network inference strategy - DDD domain model with bounded contexts Testing: - 69 tests passing across all crates - Signal processing: 45 tests - Neural networks: 21 tests - Core: 3 doc tests Performance targets: - 10x faster CSI processing (~0.5ms vs ~5ms) - 5x lower memory usage (~100MB vs ~500MB) - WASM support for browser deployment
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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