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https://github.com/saymrwulf/onnxruntime.git
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c7c8757a1c
Use ruff as the code formatter in place of black and isort since it is much faster, and as projects like PyTorch and ONNX have adopted ruff format as well. This PR include only auto-fixed changes in formatting.
119 lines
3.9 KiB
Python
119 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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