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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>
58 lines
1.6 KiB
Python
58 lines
1.6 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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.. _l-example-backend-api:
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ONNX Runtime Backend for ONNX
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=============================
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*ONNX Runtime* extends the
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`onnx backend API <https://github.com/onnx/onnx/blob/main/docs/ImplementingAnOnnxBackend.md>`_
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to run predictions using this runtime.
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Let's use the API to compute the prediction
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of a simple logistic regression model.
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"""
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import numpy as np
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from onnx import load
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import onnxruntime.backend as backend
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########################################
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# The device depends on how the package was compiled,
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# GPU or CPU.
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from onnxruntime import datasets, get_device
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from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument
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device = get_device()
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name = datasets.get_example("logreg_iris.onnx")
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model = load(name)
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rep = backend.prepare(model, device)
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x = np.array([[-1.0, -2.0]], dtype=np.float32)
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try:
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label, proba = rep.run(x)
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print(f"label={label}")
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print(f"probabilities={proba}")
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except (RuntimeError, InvalidArgument) as e:
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print(e)
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########################################
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# The backend can also directly load the model
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# without using *onnx*.
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rep = backend.prepare(name, device)
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x = np.array([[-1.0, -2.0]], dtype=np.float32)
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try:
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label, proba = rep.run(x)
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print(f"label={label}")
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print(f"probabilities={proba}")
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except (RuntimeError, InvalidArgument) as e:
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print(e)
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#######################################
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# The backend API is implemented by other frameworks
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# and makes it easier to switch between multiple runtimes
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# with the same API.
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