[GitHub] sandeep-krishnamurthy closed pull request #12297: MXNet to ONNX export tutorial

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

sandeep-krishnamurthy closed pull request #12297: MXNet to ONNX export tutorial 
URL: https://github.com/apache/incubator-mxnet/pull/12297
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/docs/api/python/contrib/onnx.md b/docs/api/python/contrib/onnx.md
index d7c34ec1e01..44994145916 100644
--- a/docs/api/python/contrib/onnx.md
+++ b/docs/api/python/contrib/onnx.md
@@ -35,6 +35,7 @@ This document describes all the ONNX-MXNet APIs.
    :maxdepth: 1
    
    /tutorials/onnx/super_resolution.md
+   /tutorials/onnx/export_mxnet_to_onnx.md
    /tutorials/onnx/inference_on_onnx_model.md
    /tutorials/onnx/fine_tuning_gluon.md
 ```
diff --git a/docs/tutorials/onnx/export_mxnet_to_onnx.md b/docs/tutorials/onnx/export_mxnet_to_onnx.md
new file mode 100644
index 00000000000..a9c03bed8b1
--- /dev/null
+++ b/docs/tutorials/onnx/export_mxnet_to_onnx.md
@@ -0,0 +1,134 @@
+
+# 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 the ONNX format.
+
+MXNet-ONNX operators coverage and features are updated regularly. Visit the [ONNX operator coverage](https://cwiki.apache.org/confluence/display/MXNET/ONNX+Operator+Coverage) page for the latest information.
+
+In this tutorial, we will learn how to use MXNet to ONNX exporter on pre-trained models.
+
+## Prerequisites
+
+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#installation) v1.2.1 (follow the install guide)
+
+*Note:* MXNet-ONNX importer and exporter follows version 7 of ONNX operator set which comes with ONNX v1.2.1.
+
+
+```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 the pre-trained ResNet-18 [ImageNet](http://www.image-net.org/) model from the [MXNet Model Zoo](http://data.mxnet.io/models/imagenet/).
+We will also download synset file to match labels.
+
+```python
+# Download pre-trained resnet model - json and params by running following code.
+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')]
+```
+
+Now, we have downloaded ResNet-18 symbol, params and synset file on the disk.
+
+## MXNet to ONNX exporter API
+
+Let us describe the MXNet's `export_model` API. 
+
+```python
+help(onnx_mxnet.export_model)
+```
+
+```python
+Help on function export_model in module mxnet.contrib.onnx.mx2onnx.export_model:
+
+export_model(sym, params, input_shape, input_type=<type 'numpy.float32'>, onnx_file_path=u'model.onnx', verbose=False)
+    Exports the MXNet model file, passed as a parameter, into ONNX model.
+    Accepts both symbol,parameter objects as well as json and params filepaths as input.
+    Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/ONNX
+    
+    Parameters
+    ----------
+    sym : str or symbol object
+        Path to the json file or Symbol object
+    params : str or symbol object
+        Path to the params file or params dictionary. (Including both arg_params and aux_params)
+    input_shape : List of tuple
+        Input shape of the model e.g [(1,3,224,224)]
+    input_type : data type
+        Input data type e.g. np.float32
+    onnx_file_path : str
+        Path where to save the generated onnx file
+    verbose : Boolean
+        If true will print logs of the model conversion
+    
+    Returns
+    -------
+    onnx_file_path : str
+        Onnx file path
+```
+
+`export_model` API can accept the MXNet model in one of the following two ways.
+
+1. 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.
+2. 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.
+
+Since we have downloaded pre-trained model files, we will use the `export_model` API by passing the path for symbol and params files.
+
+## How to use MXNet to ONNX exporter API
+
+We will use the downloaded pre-trained model files (sym, params) and define input variables.
+
+```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. The tool will validate the model by checking if the content contains valid protobuf:
+
+```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)
+```
+
+If the converted protobuf format doesn't qualify to ONNX proto specifications, the checker will throw errors, but in this case it successfully passes. 
+
+This method confirms exported model protobuf is valid. Now, the model is ready to be imported in other frameworks for inference!
+    
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/tests/tutorials/test_tutorials.py b/tests/tutorials/test_tutorials.py
index 22d00c181b6..2c8768228d7 100644
--- a/tests/tutorials/test_tutorials.py
+++ b/tests/tutorials/test_tutorials.py
@@ -124,6 +124,9 @@ def test_nlp_cnn():
 def test_onnx_super_resolution():
     assert _test_tutorial_nb('onnx/super_resolution')
 
+def test_onnx_export_mxnet_to_onnx():
+    assert _test_tutorial_nb('onnx/export_mxnet_to_onnx')
+
 def test_onnx_fine_tuning_gluon():
     assert _test_tutorial_nb('onnx/fine_tuning_gluon')
 


 

----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
users@infra.apache.org


With regards,
Apache Git Services