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### Description `lintrunner` is a linter runner successfully used by pytorch, onnx and onnx-script. It provides a uniform experience running linters locally and in CI. It supports all major dev systems: Windows, Linux and MacOs. The checks are enforced by the `Python format` workflow. This PR adopts `lintrunner` to onnxruntime and fixed ~2000 flake8 errors in Python code. `lintrunner` now runs all required python lints including `ruff`(replacing `flake8`), `black` and `isort`. Future lints like `clang-format` can be added. Most errors are auto-fixed by `ruff` and the fixes should be considered robust. Lints that are more complicated to fix are applied `# noqa` for now and should be fixed in follow up PRs. ### Notable changes 1. This PR **removed some suboptimal patterns**: - `not xxx in` -> `xxx not in` membership checks - bare excepts (`except:` -> `except Exception`) - unused imports The follow up PR will remove: - `import *` - mutable values as default in function definitions (`def func(a=[])`) - more unused imports - unused local variables 2. Use `ruff` to replace `flake8`. `ruff` is much (40x) faster than flake8 and is more robust. We are using it successfully in onnx and onnx-script. It also supports auto-fixing many flake8 errors. 3. Removed the legacy flake8 ci flow and updated docs. 4. The added workflow supports SARIF code scanning reports on github, example snapshot:  5. Removed `onnxruntime-python-checks-ci-pipeline` as redundant ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Unified linting experience in CI and local. Replacing https://github.com/microsoft/onnxruntime/pull/14306 --------- Signed-off-by: Justin Chu <justinchu@microsoft.com>
46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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"""
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Metadata
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========
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ONNX format contains metadata related to how the
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model was produced. It is useful when the model
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is deployed to production to keep track of which
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instance was used at a specific time.
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Let's see how to do that with a simple
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logistic regression model trained with
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*scikit-learn* and converted with *sklearn-onnx*.
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"""
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from onnxruntime.datasets import get_example
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example = get_example("logreg_iris.onnx")
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import onnx # noqa: E402
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model = onnx.load(example)
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print(f"doc_string={model.doc_string}")
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print(f"domain={model.domain}")
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print(f"ir_version={model.ir_version}")
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print(f"metadata_props={model.metadata_props}")
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print(f"model_version={model.model_version}")
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print(f"producer_name={model.producer_name}")
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print(f"producer_version={model.producer_version}")
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#############################
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# With *ONNX Runtime*:
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import onnxruntime as rt # noqa: E402
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sess = rt.InferenceSession(example, providers=rt.get_available_providers())
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meta = sess.get_modelmeta()
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print(f"custom_metadata_map={meta.custom_metadata_map}")
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print(f"description={meta.description}")
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print(f"domain={meta.domain}")
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print(f"graph_name={meta.graph_name}")
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print(f"producer_name={meta.producer_name}")
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print(f"version={meta.version}")
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