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https://github.com/ruvnet/RuView
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ruv
2754af804e
Replace the `Tensor::randn` stubs in occworld-candle's VQVAE encoder (`encode_occupancy`) and decoder (`decode_to_logits`) with a real, deterministic, input-dependent convolutional forward pass. Previously `predict()` emitted trajectory waypoints + confidence that were a function of RANDOM NOISE, independent of the input and silently presented as model output — the exact "AI slop" the project must eliminate. occworld-candle: - New `cnn.rs`: `Encoder2D` (3× Conv2d + GELU, interpolate2d to pin the token grid) and `Decoder2D` (upsample_nearest2d + Conv2d + 1×1 head). Both are deterministic functions of the input — same input → identical output; different input → different output. No randn in any forward path. - Deterministic weight init (`det_fill`, seeded xorshift64*) across all `dummy()` constructors (encoder/decoder, VQ codebook, quant-convs, transformer), so untrained engines are bit-for-bit reproducible. - `InferenceOutput.weights_trained: bool` — honest disclosure flag. `false` for `dummy()` (real but untrained net), `true` only after `load()` reads a real checkpoint. Priors are always from the real forward pass, never faked. - VQ codebook + quant/post-quant convs kept and wired encoder→VQ→decoder. - Centerpiece tests in `tests/predict_honesty.rs` (input-dependence, run-to-run + cross-engine determinism, untrained flag). All three FAIL on the old randn stub (verified by temporarily reinstating randn). pointcloud: - Optimize `to_gaussian_splats` hot path: 9 separate `.iter().sum()` passes per voxel → 2 fused accumulation passes. Bit-identical output. - `benches/splats_bench.rs` (criterion) measures old 9-pass vs new 2-pass with a parity guard. ~1.3× faster on representative cloud sizes. - Confirmed: no `randn`/placeholder in any claimed production path. The remaining synthetic generators (`send_test_frames`, `demo_depth_cloud`) and honestly-flagged heuristics (`heuristic_pose_from_amplitude`, luminance pseudo-depth fallback) are explicitly disclosed, not faked output. DATA-GATED: a trained checkpoint. An untrained-but-real net is the honest deliverable; accuracy is flagged via `weights_trained`, never claimed. Tests: occworld 16 unit + 3 integration + 2 doc, pointcloud 18 — all pass (CPU `Device::Cpu`; CUDA feature is GPU-gated and untouched). Co-Authored-By: claude-flow <ruv@ruv.net>
29 lines
675 B
TOML
29 lines
675 B
TOML
[package]
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name = "wifi-densepose-pointcloud"
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version = "0.1.0"
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edition = "2021"
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description = "Real-time dense point cloud from camera depth + WiFi CSI tomography"
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[[bin]]
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name = "ruview-pointcloud"
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path = "src/main.rs"
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[dependencies]
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serde = { workspace = true }
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serde_json = { workspace = true }
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tokio = { workspace = true }
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anyhow = { workspace = true }
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axum = { workspace = true }
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tower-http = { workspace = true }
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clap = { version = "4", features = ["derive"] }
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chrono = "0.4"
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dirs = "5"
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reqwest = { version = "0.12", features = ["json"], default-features = false }
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[dev-dependencies]
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criterion = { workspace = true }
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[[bench]]
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name = "splats_bench"
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harness = false
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