.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "build/examples_deployment/export_network.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_build_examples_deployment_export_network.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_build_examples_deployment_export_network.py:

1. Export trained GluonCV network to JSON
============================================

It is awesome if you are enjoy using GluonCV in Python for training and testing.
At some point, you might ask: "Is it possible to deploy the existing models to somewhere out of Python environments?"

The answer is "Absolutely!", and it's super easy actually.

This article will show you how to export networks/models to be used somewhere other than Python.


.. GENERATED FROM PYTHON SOURCE LINES 12-15

.. code-block:: default

    import gluoncv as gcv
    from gluoncv.utils import export_block








.. GENERATED FROM PYTHON SOURCE LINES 16-17

First of all, we need a network to play with, a pre-trained one is perfect

.. GENERATED FROM PYTHON SOURCE LINES 17-21

.. code-block:: default

    net = gcv.model_zoo.get_model('resnet18_v1', pretrained=True)
    export_block('resnet18_v1', net, preprocess=True, layout='HWC')
    print('Done.')





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Downloading /root/.mxnet/models/resnet18_v1-a0666292.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/resnet18_v1-a0666292.zip...

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    Done.




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.. hint::

      Use ``preprocess=True`` will add a default preprocess layer above the network,
      which will subtract mean [123.675, 116.28, 103.53], divide
      std [58.395, 57.12, 57.375], and convert original image (B, H, W, C and range [0, 255]) to
      tensor (B, C, H, W) as network input. This is the default preprocess behavior of all GluonCV
      pre-trained models. With this preprocess head, you can use raw RGB image in C++ without
      explicitly applying these operations.

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The above code generates two files: xxxx.json and xxxx.params

.. GENERATED FROM PYTHON SOURCE LINES 33-36

.. code-block:: default

    import glob
    print(glob.glob('*.json') + glob.glob('*.params'))





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    ['resnet18_v1-symbol.json', 'resnet18_v1-0000.params']




.. GENERATED FROM PYTHON SOURCE LINES 37-38

JSON file includes computational graph and params file includes pre-trained weights.

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The exportable networks are not limited to image classification models.
We can export detection and segmentation networks as well:

.. GENERATED FROM PYTHON SOURCE LINES 42-55

.. code-block:: default


    # YOLO
    net = gcv.model_zoo.get_model('yolo3_darknet53_coco', pretrained=True)
    export_block('yolo3_darknet53_coco', net)

    # FCN
    net = gcv.model_zoo.get_model('fcn_resnet50_ade', pretrained=True)
    export_block('fcn_resnet50_ade', net)

    # MaskRCNN
    net = gcv.model_zoo.get_model('mask_rcnn_resnet50_v1b_coco', pretrained=True)
    export_block('mask_rcnn_resnet50_v1b_coco', net)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Downloading /root/.mxnet/models/yolo3_darknet53_coco-09767802.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/yolo3_darknet53_coco-09767802.zip...

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.. GENERATED FROM PYTHON SOURCE LINES 56-57

We are all set here. Please checkout the other tutorials of how to use the JSON and params files.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  22.950 seconds)


.. _sphx_glr_download_build_examples_deployment_export_network.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: export_network.py <export_network.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: export_network.ipynb <export_network.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_