Address comments

Author: Thiago Crepaldi

beginner_source/export_simple_model_to_onnx_tutorial.py

@@ -5,7 +5,7 @@
 
 **Author**: `Thiago Crepaldi <https://github.com/thiagocrepaldi>`_
 
-.. Note::
+.. 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
@@ -34,7 +34,7 @@
 # 4. Visualize the ONNX model graph using `Netron <https://github.com/lutzroeder/netron>`_.
 # 5. Execute the ONNX model with `ONNX Runtime`
 #
-# Note that because the ONNX exporter uses ``onnx`` and ``onnxscript`` to translate PyTorch operators into ONNX operators,
+# Because the ONNX exporter uses ``onnx`` and ``onnxscript`` to translate PyTorch operators into ONNX operators,
 # we will need to install them.
 # %%
 #  .. code-block:: bash
@@ -85,7 +85,6 @@ def forward(self, x):
 # As we can see, we didn't need any code change on our model.
 # The resulting ONNX model is saved within ``torch.onnx.ExportOutput`` as a binary protobuf file.
 # The exporter uses static shapes by default, so the resulting model has static dimensions.
-# In a future tutorial we are going to explore how to leverage dynamic shapes and other advanced features.
 #
 # We can save it to disk with the following code:
 
@@ -98,7 +97,7 @@ def forward(self, x):
 # .. image:: ../_static/img/onnx/netron_web_ui.png
 #
 # Once Netron is open, we can drag and drop our ``my_image_classifier.onnx`` file into the browser or select it after
-# clicking on `Open model` button.
+# clicking the **Open model** button.
 #
 # .. image:: ../_static/img/onnx/image_clossifier_onnx_modelon_netron_web_ui.png
 #
@@ -117,7 +116,7 @@ def forward(self, x):
 print(f"Input length: {len(onnx_input)}")
 print(f"Sample input: {onnx_input}")
 
-# in our example, the input is the same, but we can have more inputs
+# In our example, the input is the same, but we can have more inputs
 # than the original PyTorch model in more complex cases.
 # Now we can execute the ONNX model with ONNX Runtime.
 
@@ -134,7 +133,7 @@ def to_numpy(tensor):
     return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
 
 # ONNX Runtime also requires the input to be a dictionary with
-# the keys being the input name and the value the Numpy tensor
+# the keys being the input name and the value the Numpy tensor.
 
 onnxruntime_input = {k.name: to_numpy(v) for k, v in zip(ort_session.get_inputs(), onnx_input)}
 
@@ -143,7 +142,7 @@ def to_numpy(tensor):
 onnxruntime_outputs = ort_session.run(None, onnxruntime_input)
 
 # The output can be a single tensor or a list of tensors, depending on the model.
-# Let's execute the PyTorch model and use it as benchmark next
+# Let's execute the PyTorch model and use it as benchmark next.
 torch_outputs = torch_model(torch_input)
 
 # We need to adapt the PyTorch output format to match ONNX's
@@ -158,5 +157,9 @@ def to_numpy(tensor):
 print(f"Output length: {len(onnxruntime_outputs)}")
 print(f"Sample output: {onnxruntime_outputs}")
 
+# Conclusion
+# ----------
+
 # That is about it! We have successfully exported our PyTorch model to ONNX format,
-# saved it to disk, executed it with ONNX Runtime and compared its result with PyTorch's.
+# saved the model to disk, viewed it using Netron, executed it with ONNX Runtime
+# and finally compared its numerical results with PyTorch's.

index.rst

@@ -333,7 +333,7 @@ What's new in PyTorch tutorials?
    :card_description: Build a image classifier model in PyTorch and convert it to ONNX before deploying it with ONNX Runtime.
    :image: _static/img/thumbnails/cropped/Exporting-PyTorch-Models-to-ONNX-Graphs.png
    :link: beginner/export_simple_model_to_onnx_tutorial.html
-   :tags: ONNX
+   :tags: Production,ONNX
 
 .. customcarditem::
    :header: (optional) Exporting a PyTorch Model to ONNX using TorchScript backend and Running it using ONNX Runtime