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

    Click :ref:`here <sphx_glr_download_how_to_compile_models_from_paddle.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_how_to_compile_models_from_paddle.py:


Compile PaddlePaddle Models
===========================
**Author**: `Ziyuan Ma <https://github.com/ZiyuanMa/>`_

This article is an introductory tutorial to deploy PaddlePaddle models with Relay.
For us to begin with, PaddlePaddle>=2.1.3 is required to be installed.
A quick solution is

.. code-block:: bash

    pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple

or please refer to official site.
https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html


.. code-block:: default

    import tarfile
    import paddle
    import numpy as np
    import tvm
    from tvm import relay
    from tvm.contrib.download import download_testdata





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

 Out:

 .. code-block:: none

    WARNING: OMP_NUM_THREADS set to 4, not 1. The computation speed will not be optimized if you use data parallel. It will fail if this PaddlePaddle binary is compiled with OpenBlas since OpenBlas does not support multi-threads.
    PLEASE USE OMP_NUM_THREADS WISELY.



Load pretrained ResNet50 model
---------------------------------------------
We load a pretrained ResNet50 provided by PaddlePaddle.


.. code-block:: default

    url = "https://bj.bcebos.com/x2paddle/models/paddle_resnet50.tar"
    model_path = download_testdata(url, "paddle_resnet50.tar", module="model")

    with tarfile.open(model_path) as tar:
        names = tar.getnames()
        for name in names:
            tar.extract(name, "./")

    model = paddle.jit.load("./paddle_resnet50/model")







Load a test image
---------------------------------------------
A single cat dominates the examples!


.. code-block:: default


    from PIL import Image
    import paddle.vision.transforms as T


    transforms = T.Compose(
        [
            T.Resize((256, 256)),
            T.CenterCrop(224),
            T.ToTensor(),
            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ]
    )

    img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
    img_path = download_testdata(img_url, "cat.png", module="data")
    img = Image.open(img_path).resize((224, 224))

    img = transforms(img)
    img = np.expand_dims(img, axis=0)







Compile the model with relay
---------------------------------------------


.. code-block:: default


    target = "llvm"
    shape_dict = {"inputs": img.shape}
    mod, params = relay.frontend.from_paddle(model, shape_dict)

    with tvm.transform.PassContext(opt_level=3):
        executor = relay.build_module.create_executor(
            "graph", mod, tvm.cpu(0), target, params
        ).evaluate()







Execute on TVM
---------------------------------------------


.. code-block:: default

    dtype = "float32"
    tvm_output = executor(tvm.nd.array(img.astype(dtype))).numpy()







Look up synset name
---------------------------------------------
Look up prediction top 1 index in 1000 class synset.


.. code-block:: default


    synset_url = "".join(
        [
            "https://gist.githubusercontent.com/zhreshold/",
            "4d0b62f3d01426887599d4f7ede23ee5/raw/",
            "596b27d23537e5a1b5751d2b0481ef172f58b539/",
            "imagenet1000_clsid_to_human.txt",
        ]
    )
    synset_name = "imagenet1000_clsid_to_human.txt"
    synset_path = download_testdata(synset_url, synset_name, module="data")
    with open(synset_path) as f:
        synset = f.readlines()

    top1 = np.argmax(tvm_output[0])
    print(f"TVM prediction top-1 id: {top1}, class name: {synset[top1]}")




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

 Out:

 .. code-block:: none

    TVM prediction top-1 id: 282, class name:  282: 'tiger cat',





.. _sphx_glr_download_how_to_compile_models_from_paddle.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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