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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>
118 lines
3.9 KiB
Python
118 lines
3.9 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-common-error:
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Common errors with onnxruntime
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==============================
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This example looks into several common situations
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in which *onnxruntime* does not return the model
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prediction but raises an exception instead.
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It starts by loading the model trained in example
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:ref:`l-logreg-example` which produced a logistic regression
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trained on *Iris* datasets. The model takes
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a vector of dimension 2 and returns a class among three.
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"""
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import numpy
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import onnxruntime as rt
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from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument
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from onnxruntime.datasets import get_example
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example2 = get_example("logreg_iris.onnx")
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sess = rt.InferenceSession(example2, providers=rt.get_available_providers())
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input_name = sess.get_inputs()[0].name
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output_name = sess.get_outputs()[0].name
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#############################
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# The first example fails due to *bad types*.
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# *onnxruntime* only expects single floats (4 bytes)
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# and cannot handle any other kind of floats.
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try:
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x = numpy.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=numpy.float64)
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sess.run([output_name], {input_name: x})
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except Exception as e:
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print("Unexpected type")
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print(f"{type(e)}: {e}")
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#########################
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# The model fails to return an output if the name
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# is misspelled.
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try:
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x = numpy.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=numpy.float32)
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sess.run(["misspelled"], {input_name: x})
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except Exception as e:
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print("Misspelled output name")
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print(f"{type(e)}: {e}")
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###########################
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# The output name is optional, it can be replaced by *None*
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# and *onnxruntime* will then return all the outputs.
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x = numpy.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=numpy.float32)
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try:
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res = sess.run(None, {input_name: x})
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print("All outputs")
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print(res)
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except (RuntimeError, InvalidArgument) as e:
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print(e)
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#########################
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# The same goes if the input name is misspelled.
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try:
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x = numpy.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=numpy.float32)
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sess.run([output_name], {"misspelled": x})
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except Exception as e:
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print("Misspelled input name")
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print(f"{type(e)}: {e}")
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#########################
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# *onnxruntime* does not necessarily fail if the input
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# dimension is a multiple of the expected input dimension.
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for x in [
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numpy.array([1.0, 2.0, 3.0, 4.0], dtype=numpy.float32),
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numpy.array([[1.0, 2.0, 3.0, 4.0]], dtype=numpy.float32),
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numpy.array([[1.0, 2.0], [3.0, 4.0]], dtype=numpy.float32),
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numpy.array([1.0, 2.0, 3.0], dtype=numpy.float32),
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numpy.array([[1.0, 2.0, 3.0]], dtype=numpy.float32),
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]:
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try:
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r = sess.run([output_name], {input_name: x})
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print(f"Shape={x.shape} and predicted labels={r}")
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except (RuntimeError, InvalidArgument) as e:
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print(f"ERROR with Shape={x.shape} - {e}")
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for x in [
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numpy.array([1.0, 2.0, 3.0, 4.0], dtype=numpy.float32),
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numpy.array([[1.0, 2.0, 3.0, 4.0]], dtype=numpy.float32),
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numpy.array([[1.0, 2.0], [3.0, 4.0]], dtype=numpy.float32),
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numpy.array([1.0, 2.0, 3.0], dtype=numpy.float32),
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numpy.array([[1.0, 2.0, 3.0]], dtype=numpy.float32),
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]:
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try:
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r = sess.run(None, {input_name: x})
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print(f"Shape={x.shape} and predicted probabilities={r[1]}")
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except (RuntimeError, InvalidArgument) as e:
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print(f"ERROR with Shape={x.shape} - {e}")
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#########################
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# It does not fail either if the number of dimension
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# is higher than expects but produces a warning.
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for x in [
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numpy.array([[[1.0, 2.0], [3.0, 4.0]]], dtype=numpy.float32),
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numpy.array([[[1.0, 2.0, 3.0]]], dtype=numpy.float32),
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numpy.array([[[1.0, 2.0]], [[3.0, 4.0]]], dtype=numpy.float32),
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]:
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try:
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r = sess.run([output_name], {input_name: x})
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print(f"Shape={x.shape} and predicted labels={r}")
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except (RuntimeError, InvalidArgument) as e:
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print(f"ERROR with Shape={x.shape} - {e}")
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