Huang Qi
c8fa5db7d3
Coverage: - test_model: SimpleNet forward (parametrized over batch sizes and both unsqueezed and flat input shapes), layer dimensions, differentiability, and ONNX round-trip - test_inference: load_model resolution order (bundled, cwd override, explicit path, missing path), and predict shape/dtype/determinism plus endpoint sanity across 8 cardinal/diagonal targets - test_train: _load_csv parsing, TrajectoryDataset indexing, full train() pipeline producing a single-file ONNX, plus a smoke test against the real data shipped under data/ - test_cli: --help for the three console scripts and a real run of mouse-visualize via both the entry point and python -m Wire up pytest via dependency-groups and tool.pytest.ini_options.
52 lines
1.2 KiB
TOML
52 lines
1.2 KiB
TOML
[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[project]
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name = "mouse-control"
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version = "0.1.0"
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description = "Neural-network mouse trajectory generation that mimics human motion."
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readme = "README.md"
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requires-python = ">=3.12"
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keywords = ["mouse", "trajectory", "neural-network", "onnx", "anti-detection"]
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classifiers = [
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3.12",
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"Operating System :: Microsoft :: Windows",
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"Topic :: Scientific/Engineering :: Artificial Intelligence",
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]
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dependencies = [
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"matplotlib>=3.10",
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"numpy>=2.0",
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"onnxruntime>=1.20",
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"onnxscript>=0.7",
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"pandas>=2.0",
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"scipy>=1.14",
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"torch>=2.5",
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]
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[project.scripts]
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mouse-collect = "mouse_control.collect:main"
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mouse-train = "mouse_control.train:main"
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mouse-visualize = "mouse_control.visualize:main"
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[dependency-groups]
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dev = ["pytest>=8.0"]
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[tool.pytest.ini_options]
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testpaths = ["tests"]
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addopts = "-ra"
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[tool.hatch.build.targets.wheel]
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packages = ["src/mouse_control"]
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artifacts = ["src/mouse_control/assets/*.onnx"]
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[tool.hatch.build.targets.sdist]
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include = [
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"src/",
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"data/",
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"imgs/",
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"README.md",
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"pyproject.toml",
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]
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