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43 lines
1.4 KiB
Python
43 lines
1.4 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
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model = onnx.load(example)
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print("doc_string={}".format(model.doc_string))
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print("domain={}".format(model.domain))
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print("ir_version={}".format(model.ir_version))
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print("metadata_props={}".format(model.metadata_props))
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print("model_version={}".format(model.model_version))
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print("producer_name={}".format(model.producer_name))
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print("producer_version={}".format(model.producer_version))
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#############################
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# With *ONNX Runtime*:
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from onnxruntime import InferenceSession
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sess = InferenceSession(example)
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meta = sess.get_modelmeta()
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print("custom_metadata_map={}".format(meta.custom_metadata_map))
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print("description={}".format(meta.description))
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print("domain={}".format(meta.domain, meta.domain))
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print("graph_name={}".format(meta.graph_name))
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print("producer_name={}".format(meta.producer_name))
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print("version={}".format(meta.version))
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