Merge branch 'main' into angelayi/export_fix

Author: svekars

.jenkins/validate_tutorials_built.py

@@ -51,8 +51,6 @@
     "intermediate_source/text_to_speech_with_torchaudio",
     "intermediate_source/tensorboard_profiler_tutorial", # reenable after 2.0 release.
     "advanced_source/pendulum",
-    "beginner_source/onnx/export_simple_model_to_onnx_tutorial",
-    "beginner_source/onnx/onnx_registry_tutorial"
 ]
 
 def tutorial_source_dirs() -> List[Path]:

_static/img/onnx/custom_addandround.png

@@ -7,7 +7,7 @@
 Export a PyTorch model to ONNX
 ==============================
 
-**Author**: `Thiago Crepaldi <https://github.com/thiagocrepaldi>`_
+**Author**: `Ti-Tai Wang <https://github.com/titaiwangms>`_ and `Xavier Dupré <https://github.com/xadupre>`_
 
 .. note::
     As of PyTorch 2.1, there are two versions of ONNX Exporter.
@@ -127,7 +127,7 @@ def forward(self, x):
 # Once Netron is open, we can drag and drop our ``my_image_classifier.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%
 #
 #
@@ -155,7 +155,7 @@ def forward(self, x):
 
 import onnxruntime
 
-onnx_input = onnx_program.adapt_torch_inputs_to_onnx(torch_input)
+onnx_input = [torch_input]
 print(f"Input length: {len(onnx_input)}")
 print(f"Sample input: {onnx_input}")
 
@@ -166,7 +166,8 @@ def to_numpy(tensor):
 
 onnxruntime_input = {k.name: to_numpy(v) for k, v in zip(ort_session.get_inputs(), onnx_input)}
 
-onnxruntime_outputs = ort_session.run(None, onnxruntime_input)
+# onnxruntime 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
@@ -179,7 +180,6 @@ def to_numpy(tensor):
 # 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)
 
 assert len(torch_outputs) == len(onnxruntime_outputs)
 for torch_output, onnxruntime_output in zip(torch_outputs, onnxruntime_outputs):