e99a41434d
- Workspace version: 0.2.0 → 0.3.0 - All internal path dependency versions updated - ruvector-crv/gnn gated behind optional `crv` feature (removed [patch.crates-io]) - All 15 crates published to crates.io at v0.3.0 Published crates (in order): 1. wifi-densepose-core 2. wifi-densepose-vitals 3. wifi-densepose-wifiscan 4. wifi-densepose-hardware 5. wifi-densepose-config 6. wifi-densepose-db 7. wifi-densepose-signal 8. wifi-densepose-nn 9. wifi-densepose-ruvector 10. wifi-densepose-api 11. wifi-densepose-train 12. wifi-densepose-mat 13. wifi-densepose-wasm 14. wifi-densepose-sensing-server 15. wifi-densepose-cli Co-Authored-By: claude-flow <ruv@ruv.net>
92 lines
2.3 KiB
TOML
92 lines
2.3 KiB
TOML
[package]
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name = "wifi-densepose-train"
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version = "0.3.0"
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edition = "2021"
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authors = ["rUv <ruv@ruv.net>", "WiFi-DensePose Contributors"]
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license = "MIT OR Apache-2.0"
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description = "Training pipeline for WiFi-DensePose pose estimation"
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repository = "https://github.com/ruvnet/wifi-densepose"
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documentation = "https://docs.rs/wifi-densepose-train"
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keywords = ["wifi", "training", "pose-estimation", "deep-learning"]
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categories = ["science", "computer-vision"]
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readme = "README.md"
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[[bin]]
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name = "train"
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path = "src/bin/train.rs"
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[[bin]]
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name = "verify-training"
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path = "src/bin/verify_training.rs"
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required-features = ["tch-backend"]
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[features]
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default = []
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tch-backend = ["tch"]
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cuda = ["tch-backend"]
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[dependencies]
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# Internal crates
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wifi-densepose-signal = { version = "0.3.0", path = "../wifi-densepose-signal" }
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wifi-densepose-nn = { version = "0.3.0", path = "../wifi-densepose-nn" }
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# Core
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thiserror.workspace = true
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anyhow.workspace = true
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serde = { workspace = true, features = ["derive"] }
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serde_json.workspace = true
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# Tensor / math
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ndarray.workspace = true
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num-complex.workspace = true
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num-traits.workspace = true
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# PyTorch bindings (optional — only enabled by `tch-backend` feature)
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tch = { workspace = true, optional = true }
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# Graph algorithms (min-cut for optimal keypoint assignment)
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petgraph.workspace = true
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# ruvector integration (subpolynomial min-cut, sparse solvers, temporal compression, attention)
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ruvector-mincut = { workspace = true }
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ruvector-attn-mincut = { workspace = true }
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ruvector-temporal-tensor = { workspace = true }
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ruvector-solver = { workspace = true }
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ruvector-attention = { workspace = true }
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# Data loading
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ndarray-npy.workspace = true
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memmap2 = "0.9"
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walkdir.workspace = true
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# Serialization
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csv.workspace = true
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toml = "0.8"
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# Logging / progress
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tracing.workspace = true
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tracing-subscriber.workspace = true
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indicatif.workspace = true
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# Async (subset of features needed by training pipeline)
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tokio = { workspace = true, features = ["rt", "rt-multi-thread", "macros", "fs"] }
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# Crypto (for proof hash)
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sha2.workspace = true
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# CLI
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clap.workspace = true
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# Time
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chrono = { version = "0.4", features = ["serde"] }
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[dev-dependencies]
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criterion.workspace = true
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proptest.workspace = true
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tempfile = "3.10"
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approx = "0.5"
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[[bench]]
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name = "training_bench"
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harness = false
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