julian
f7a262ac9a
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apps/trainer (parking-trainer): inspect / train / evaluate / publish. Reads the wash collector's SQLite + crops read-only off its volume; time split (validation = newest slice); thin classes dropped; damped class weights; `features` mode (frozen ImageNet backbone, on-disk feature cache, seconds to retrain) and `finetune` mode (light augmentation). CPU-only torch from PyTorch's wheel index. ONNX export checked against the torch model; NO model file below the validation floor (exit 3, report still written); exit 2 = not enough labels. `evaluate` scores a shipped model on labels reviewed after training + the unlabelled pile; `publish` PUTs a version folder to a Gitea generic package. Light core deps; the `train` extra is heavy — CI syncs without it, torch tests skip. apps/vision: BodyTypeClassifier (bodytype.onnx + sidecar = the preprocessing contract: crop margin, input size, RGB 0-255, normalisation inside the graph) and RefinedVehicleDetector over YOLOX — refines only `car` or a class the classifier trained on, min-confidence, `detector_class` on the result; path set but no file = phase B off without an error; a broken file is a health detail. models/bodytype.version (tracked, empty) pins the published version the Dockerfile fetches at build (BuildKit secret; a pin that cannot be fetched fails the build). Verified: a trainer model gives identical probabilities inside the vision service; both images built and smoke-tested. Delivery: parking-trainer image in build-images.yml, the `trainer` compose profile on the collector stack (CPU, read-only data, TRAINER_OUT), commented TRAINER_OUT/PUBLISH_TOKEN in the wash-collector stack, .dockerignore for both Python contexts, trainer deps synced in CI. Wiki: bodytype-classifier-training rewritten as built (+ one fleet model not per site, secrets/access, where the crops live), opencv-anpr-service §Phase B, vision-review-outbox, vision-service-packaging, fleet-deployment-komodo, index, log. Claude-Session: https://claude.ai/code/session_01FWncR69HgGPuei1dLrW3cU
74 lines
2.3 KiB
TOML
74 lines
2.3 KiB
TOML
[project]
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name = "parking-trainer"
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version = "0.0.0"
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description = "Phase-B body-type classifier trainer: reviewer labels + crops off the wash collector's volume → an ONNX classifier the vision image bakes in."
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requires-python = ">=3.10,<4.0"
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# Core deps are LIGHT on purpose (same rule as the vision service): `inspect`, `evaluate`
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# and the data/report code run with only these, so `uv sync` and the test suite work
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# in CI without the PyTorch stack. Training itself needs the `train` extra.
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# See wiki/decisions/bodytype-classifier-training.md.
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dependencies = [
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"numpy>=1.26",
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# OpenCV does the decode + resize on BOTH sides (trainer and vision service): same
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# library, same interpolation, same pixels — the preprocessing contract (preprocess.py).
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"opencv-python-headless>=4.10",
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"onnxruntime>=1.19",
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]
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[project.scripts]
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parking-trainer = "trainer.cli:main"
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[project.optional-dependencies]
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# The training stack. CPU-only PyTorch (the reviewer's host has no usable GPU — the
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# decision is recorded in the wiki page above): resolved from PyTorch's CPU wheel index,
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# ~200 MB instead of the ~5 GB CUDA build. Install with: uv sync --extra train
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# torch / torchvision are BSD-3; the ImageNet backbone weights ship under the same
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# licence (the licence rule applies to weights as much as code).
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train = [
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"torch>=2.4",
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"torchvision>=0.19",
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"onnx>=1.16",
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"onnxscript>=0.3", # the torch.export-based ONNX exporter (MIT)
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]
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[dependency-groups]
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dev = [
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"ruff>=0.8",
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"pytest>=8.3",
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"mypy>=1.13",
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]
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[tool.uv]
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# Pick the CPU wheels for torch/torchvision from PyTorch's own index; everything else
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# from PyPI. `explicit = true` keeps the index from shadowing PyPI for other packages.
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[[tool.uv.index]]
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name = "pytorch-cpu"
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url = "https://download.pytorch.org/whl/cpu"
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explicit = true
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[tool.uv.sources]
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torch = [{ index = "pytorch-cpu" }]
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torchvision = [{ index = "pytorch-cpu" }]
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[tool.ruff]
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line-length = 110
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target-version = "py310"
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[tool.ruff.lint]
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select = ["E", "F", "I", "B", "UP"]
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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[tool.mypy]
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python_version = "3.12"
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strict = true
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ignore_missing_imports = true
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["trainer"]
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