[GitHub] aaronmarkham commented on a change in pull request #12297: MXNet to ONNX export tutorial

[GitBox <gi...@apache.org>]

aaronmarkham commented on a change in pull request #12297: MXNet to ONNX export tutorial 
URL: https://github.com/apache/incubator-mxnet/pull/12297#discussion_r212142209
 
 

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 File path: docs/tutorials/onnx/export_mxnet_to_onnx.md
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+
+# Exporting MXNet model to ONNX format
+
+[Open Neural Network Exchange (ONNX)](https://github.com/onnx/onnx) provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
+
+In this tutorial, we will show how you can save MXNet models to ONNX format.
+
+Current MXNet-ONNX import and export operator support and coverage can be found [here](https://cwiki.apache.org/confluence/display/MXNET/ONNX+Operator+Coverage).
+
+In this tutorial we will:
+    
+- learn how to use MXNet to ONNX exporter on pretrained models
+
+## Pre-requisite
+
+To run the tutorial you will need to have installed the following python modules:
+- [MXNet == 1.3.0](http://mxnet.incubator.apache.org/install/index.html)
+- [onnx](https://github.com/onnx/onnx) v1.2.1 (follow the install guide)
+MXNet ONNX importer and exporter follows version 7 of ONNX operator set.
+
+
+```python
+import mxnet as mx
+import numpy as np
+from mxnet.contrib import onnx as onnx_mxnet
+import logging
+logging.basicConfig(level=logging.INFO)
+```
+
+## Downloading a model from the MXNet model zoo
+
+We download a pre-trained model, in this case resnet-18 model, trained on [ImageNet](http://www.image-net.org/) from the [MXNet Model Zoo](http://data.mxnet.io/models/imagenet/).
+We will also download synset file to match labels.
+
+```python
+# Download pretrained resnet model - json and params from mxnet model zoo.
+path='http://data.mxnet.io/models/imagenet/'
+[mx.test_utils.download(path+'resnet/18-layers/resnet-18-0000.params'),
+ mx.test_utils.download(path+'resnet/18-layers/resnet-18-symbol.json'),
+ mx.test_utils.download(path+'synset.txt')]
+```
+
+## MXNet to ONNX exporter API
+
+We can check MXNet's ONNX `export_model` API usage as follows: 
+
+```python
+help(onnx_mxnet.export_model)
+```
+
+From the above API description, we can see that export_model API accepts 2 kinds of inputs:
+
+#### MXNet sym, params objects:
+
+This is useful if we are training a model. At the end of training, we just need to invoke the `export_model` function and provide sym and params objects as inputs with other attributes to save the model in ONNX format.
+
+#### MXNet's exported json and params files:
+
+This is useful if we have pre-trained models and we want to convert them to ONNX format.
+
+In this tutorial, we will show second usecase to convert pretrained model to ONNX format:
+
+## How to use MXNet to ONNX exporter API
+
+```python
+# Downloaded input symbol and params files
+sym = 'resnet-18-symbol.json'
+params = 'resnet-18-0000.params'
+# Standard Imagenet input - 3 channels, 224*224
+input_shape = (1,3,224,224)
+# Path of the output file
+onnx_file = 'mxnet_exported_resnet50.onnx'
+```
+
+We have defined the input parameters required for the `export_model` API. Now, we are ready to covert the MXNet model into ONNX format.
+
+```python
+# Invoke export model API. It returns path of the converted onnx model
+converted_model_path = onnx_mxnet.export_model(sym, params, [input_shape], np.float32, onnx_file)
+```
+
+This API returns path of the converted model which you can later use to import the model into other frameworks.
+
+## Check validity of ONNX model
+
+Now we can check validity of the converted ONNX model by using ONNX checker tool as follows:
+
+```python
+from onnx import checker
+import onnx
+# Load onnx model
+model_proto = onnx.load(converted_model_path)
+
+# Check if converted ONNX protobuf is valid
+checker.check_graph(model_proto.graph)
+```
+
+This method confirms exported model protobuf is valid. Now, the model is ready to be imported in other frameworks for inference!
 
 Review comment:
   Any comment on what to do if the check fails or common reasons why it might fail?
   
   Also, is there a call to action or next steps?

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