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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>
70 lines
1.6 KiB
Python
70 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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Draw a pipeline
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===============
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There is no other way to look into one model stored
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in ONNX format than looking into its node with
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*onnx*. This example demonstrates
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how to draw a model and to retrieve it in *json*
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format.
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.. contents::
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:local:
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Retrieve a model in JSON format
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+++++++++++++++++++++++++++++++
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That's the most simple way.
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"""
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from onnxruntime.datasets import get_example
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example1 = get_example("mul_1.onnx")
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import onnx # noqa: E402
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model = onnx.load(example1) # model is a ModelProto protobuf message
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print(model)
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#################################
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# Draw a model with ONNX
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# ++++++++++++++++++++++
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# We use `net_drawer.py <https://github.com/onnx/onnx/blob/main/onnx/tools/net_drawer.py>`_
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# included in *onnx* package.
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# We use *onnx* to load the model
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# in a different way than before.
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from onnx import ModelProto # noqa: E402
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model = ModelProto()
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with open(example1, "rb") as fid:
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content = fid.read()
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model.ParseFromString(content)
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###################################
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# We convert it into a graph.
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from onnx.tools.net_drawer import GetOpNodeProducer, GetPydotGraph # noqa: E402
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pydot_graph = GetPydotGraph(
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model.graph, name=model.graph.name, rankdir="LR", node_producer=GetOpNodeProducer("docstring")
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)
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pydot_graph.write_dot("graph.dot")
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#######################################
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# Then into an image
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import os # noqa: E402
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os.system("dot -O -Tpng graph.dot")
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################################
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# Which we display...
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import matplotlib.pyplot as plt # noqa: E402
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image = plt.imread("graph.dot.png")
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plt.imshow(image)
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