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Oct 18, 2025
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beginner_source/onnx/export_simple_model_to_onnx_tutorial.py 영어 문서 최신화 #1043

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english tutorial update
wnsx0000 Oct 17, 2025
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english tutorial update
wnsx0000 Oct 17, 2025
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62 changes: 31 additions & 31 deletions beginner_source/onnx/export_simple_model_to_onnx_tutorial.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,26 +2,26 @@
"""
`Introduction to ONNX <intro_onnx.html>`_ ||
**Exporting a PyTorch model to ONNX** ||
`Extending the ONNX Registry <onnx_registry_tutorial.html>`_
`Extending the ONNX exporter operator support <onnx_registry_tutorial.html>`_ ||
`Export a model with control flow to ONNX <export_control_flow_model_to_onnx_tutorial.html>`_

Export a PyTorch model to ONNX
==============================

**Author**: `Thiago Crepaldi <https://github.com/thiagocrepaldi>`_
**Author**: `Ti-Tai Wang <https://github.com/titaiwangms>`_, `Justin Chu <[email protected]>`_, `Thiago Crepaldi <https://github.com/thiagocrepaldi>`_.

.. note::
As of PyTorch 2.1, there are two versions of ONNX Exporter.

* ``torch.onnx.dynamo_export`` is the newest (still in beta) exporter based on the TorchDynamo technology released with PyTorch 2.0
* ``torch.onnx.export`` is based on TorchScript backend and has been available since PyTorch 1.2.0
Starting with PyTorch 2.5, there are two ONNX Exporter options available.
* ``torch.onnx.export(..., dynamo=True)`` is the recommended exporter that leverages ``torch.export`` and Torch FX for graph capture.
* ``torch.onnx.export`` is the legacy approach that relies on the deprecated TorchScript and is no longer recommended for use.

"""

###############################################################################
# In the `60 Minute Blitz <https://tutorials.pytorch.kr/beginner/deep_learning_60min_blitz.html>`_,
# we had the opportunity to learn about PyTorch at a high level and train a small neural network to classify images.
# In this tutorial, we are going to expand this to describe how to convert a model defined in PyTorch into the
# ONNX format using TorchDynamo and the ``torch.onnx.dynamo_export`` ONNX exporter.
# ONNX format using the ``torch.onnx.export(..., dynamo=True)`` ONNX exporter.
#
# While PyTorch is great for iterating on the development of models, the model can be deployed to production
# using different formats, including `ONNX <https://onnx.ai/>`_ (Open Neural Network Exchange)!
Expand All @@ -47,8 +47,7 @@
#
# .. code-block:: bash
#
# pip install onnx
# pip install onnxscript
# pip install --upgrade onnx onnxscript
#
# 2. Author a simple image classifier model
# -----------------------------------------
Expand All @@ -62,17 +61,16 @@
import torch.nn.functional as F


class MyModel(nn.Module):

class ImageClassifierModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
super().__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)

def forward(self, x):
def forward(self, x: torch.Tensor):
x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), 2)
x = torch.flatten(x, 1)
Expand All @@ -81,16 +79,18 @@ def forward(self, x):
x = self.fc3(x)
return x


######################################################################
# 3. Export the model to ONNX format
# ----------------------------------
#
# Now that we have our model defined, we need to instantiate it and create a random 32x32 input.
# Next, we can export the model to ONNX format.

torch_model = MyModel()
torch_input = torch.randn(1, 1, 32, 32)
onnx_program = torch.onnx.dynamo_export(torch_model, torch_input)
torch_model = ImageClassifierModel()
# Create example inputs for exporting the model. The inputs should be a tuple of tensors.
example_inputs = (torch.randn(1, 1, 32, 32),)
onnx_program = torch.onnx.export(torch_model, example_inputs, dynamo=True)

######################################################################
# As we can see, we didn't need any code change to the model.
Expand All @@ -102,13 +102,14 @@ def forward(self, x):
# Although having the exported model loaded in memory is useful in many applications,
# we can save it to disk with the following code:

onnx_program.save("my_image_classifier.onnx")
onnx_program.save("image_classifier_model.onnx")

######################################################################
# You can load the ONNX file back into memory and check if it is well formed with the following code:

import onnx
onnx_model = onnx.load("my_image_classifier.onnx")

onnx_model = onnx.load("image_classifier_model.onnx")
onnx.checker.check_model(onnx_model)

######################################################################
Expand All @@ -124,10 +125,10 @@ def forward(self, x):
# :align: center
#
#
# Once Netron is open, we can drag and drop our ``my_image_classifier.onnx`` file into the browser or select it after
# Once Netron is open, we can drag and drop our ``image_classifier_model.onnx`` file into the browser or select it after
# clicking the **Open model** button.
#
# .. image:: ../../_static/img/onnx/image_clossifier_onnx_modelon_netron_web_ui.png
# .. image:: ../../_static/img/onnx/image_classifier_onnx_model_on_netron_web_ui.png
# :width: 50%
#
#
Expand Down Expand Up @@ -155,18 +156,18 @@ def forward(self, x):

import onnxruntime

onnx_input = onnx_program.adapt_torch_inputs_to_onnx(torch_input)
print(f"Input length: {len(onnx_input)}")
print(f"Sample input: {onnx_input}")

ort_session = onnxruntime.InferenceSession("./my_image_classifier.onnx", providers=['CPUExecutionProvider'])
onnx_inputs = [tensor.numpy(force=True) for tensor in example_inputs]
print(f"Input length: {len(onnx_inputs)}")
print(f"Sample input: {onnx_inputs}")

def to_numpy(tensor):
return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
ort_session = onnxruntime.InferenceSession(
"./image_classifier_model.onnx", providers=["CPUExecutionProvider"]
)

onnxruntime_input = {k.name: to_numpy(v) for k, v in zip(ort_session.get_inputs(), onnx_input)}
onnxruntime_input = {input_arg.name: input_value for input_arg, input_value in zip(ort_session.get_inputs(), onnx_inputs)}

onnxruntime_outputs = ort_session.run(None, onnxruntime_input)
# ONNX Runtime returns a list of outputs
onnxruntime_outputs = ort_session.run(None, onnxruntime_input)[0]

####################################################################
# 7. Compare the PyTorch results with the ones from the ONNX Runtime
Expand All @@ -178,8 +179,7 @@ def to_numpy(tensor):
# For that, we need to execute the PyTorch model with the same input and compare the results with ONNX Runtime's.
# Before comparing the results, we need to convert the PyTorch's output to match ONNX's format.

torch_outputs = torch_model(torch_input)
torch_outputs = onnx_program.adapt_torch_outputs_to_onnx(torch_outputs)
torch_outputs = torch_model(*example_inputs)

assert len(torch_outputs) == len(onnxruntime_outputs)
for torch_output, onnxruntime_output in zip(torch_outputs, onnxruntime_outputs):
Expand Down