dtourolle
95c9cffc0d
On the tablet the engine compiled arcface for the NPU: the routing compared the form a rung wants with the form on offer, and for the embedder both are f32, so nothing said no. A rung now says which roles it serves at all, and the Hexagon does not serve the embedder (§7 — its vectors must compare across devices). Tested at the routing seam. dr-segment's onnx_probe example still named ort-tract, which is what stopped the workspace test build.
61 lines
2.5 KiB
TOML
61 lines
2.5 KiB
TOML
[package]
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name = "dr-segment"
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version.workspace = true
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edition.workspace = true
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rust-version.workspace = true
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license.workspace = true
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# Guards against a Git LFS pointer being embedded in place of the weights.
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build = "build.rs"
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[dependencies]
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thiserror.workspace = true
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log.workspace = true
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# Inference. `ort` is the API; **what runs it is `dr-inference-engine`'s
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# business** — tract, or an ONNX Runtime the app found on disk, on whichever
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# provider the device has (docs/inference.md). This crate never names either.
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ort = { workspace = true, optional = true }
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dr-inference-engine = { workspace = true, optional = true }
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ndarray = { workspace = true, optional = true }
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[dev-dependencies]
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# The example reads an ordinary JPEG, because the thing worth looking at is
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# whether detections land on a real photograph. Pure Rust, and already in the
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# tree for embedded previews.
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zune-jpeg.workspace = true
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env_logger.workspace = true
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# The probe example drives `ort` directly to print the raw load error, and
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# asks the engine for a runtime by name rather than naming one itself.
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dr-inference-engine.workspace = true
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[features]
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# On by default: a local adjustment that cannot select a subject is half the
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# feature, and the whole point of the tract backend is that enabling this costs
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# no C dependency on any platform.
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default = ["semantic", "embedded-model"]
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# Arm B — the ONNX runtime and the instance decoder.
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#
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# Separable because the watershed half is genuinely independent of it: with
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# this off, `dr-segment` is a pure-CPU graph algorithm crate with no model to
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# carry, which is what the headless hierarchy tests want.
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semantic = ["dep:ort", "dep:dr-inference-engine", "dep:ndarray"]
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# Compile the weights into the binary.
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#
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# Separate from `semantic` because the two answer different questions. Android
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# hands the app no filesystem path to read a model from (ARCH §6.9), so there
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# it must be embedded; a desktop packager pointing at a system model directory,
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# or a test that only needs the decoder, wants the runtime without the 11 MB.
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embedded-model = ["semantic"]
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# Compile the *scene* model in too, and off by default where `embedded-model`
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# is on.
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#
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# The asymmetry is its size. At 24 MB it is more than twice the instance model,
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# and Android reaches it the way it reaches the face weights — unpacked from
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# APK assets at first launch — rather than by carrying it in the binary. This
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# feature is for a desktop build with nowhere else to read it from, and for
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# tests that want the real graph.
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embedded-scene-model = ["semantic"]
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