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867804bea1
In addition to ORTModule auto documentation during packaging, this PR also update golden numbers to fix CI
70 lines
1.8 KiB
Python
70 lines
1.8 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-profiling:
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Profile the execution of a simple model
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=======================================
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*ONNX Runtime* can profile the execution of the model.
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This example shows how to interpret the results.
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"""
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import onnx
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import onnxruntime as rt
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import numpy
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from onnxruntime.datasets import get_example
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def change_ir_version(filename, ir_version=6):
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"onnxruntime==1.2.0 does not support opset <= 7 and ir_version > 6"
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with open(filename, "rb") as f:
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model = onnx.load(f)
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model.ir_version = 6
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if model.opset_import[0].version <= 7:
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model.opset_import[0].version = 11
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return model
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#########################
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# Let's load a very simple model and compute some prediction.
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example1 = get_example("mul_1.onnx")
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onnx_model = change_ir_version(example1)
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onnx_model_str = onnx_model.SerializeToString()
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sess = rt.InferenceSession(onnx_model_str)
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input_name = sess.get_inputs()[0].name
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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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res = sess.run(None, {input_name: x})
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print(res)
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#########################
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# We need to enable to profiling
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# before running the predictions.
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options = rt.SessionOptions()
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options.enable_profiling = True
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sess_profile = rt.InferenceSession(onnx_model_str, options)
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input_name = sess.get_inputs()[0].name
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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(None, {input_name: x})
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prof_file = sess_profile.end_profiling()
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print(prof_file)
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###########################
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# The results are stored un a file in JSON format.
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# Let's see what it contains.
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import json
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with open(prof_file, "r") as f:
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sess_time = json.load(f)
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import pprint
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pprint.pprint(sess_time)
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