.. only:: html

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

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

    .. _sphx_glr_tutorials_load_paddle_model.py:


Load and Execute Paddle Model
=====================

In this tutorial, we will show you how to load and execute a paddle model in CINN.
We offer you four optional models: ResNet50, MobileNetV2, EfficientNet and FaceDet.


.. code-block:: python


    import paddle
    import paddle.fluid as fluid
    import cinn
    from cinn import *
    from cinn.frontend import *
    from cinn.framework import *
    from cinn.common import *
    import numpy as np
    import os
    import sys
    # sphinx_gallery_thumbnail_path = './paddlepaddle.png'





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

 Out:

 .. code-block:: none

    /WorkSpace/CINN/build/ci-env/lib/python3.6/site-packages/setuptools/depends.py:2: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses
      import imp




**Prepare to Load Model**
--------------------------
Declare the params and prepare to load and execute the paddle model.

- :code:`model_dir` is the path where the paddle model is stored.

- :code:`input_tensor` is the name of input tensor in the model.

- :code:`target_tensor` is the name of output tensor we want.

- :code:`x_shape` is the input tensor's shape of the model

- When choosing model ResNet50, the params should be ::

      model_dir = "./ResNet50"

      input_tensor = 'inputs'

      target_tensor = 'save_infer_model/scale_0.tmp_1'

      x_shape = [1, 3, 224, 224]

- When choosing model MobileNetV2, the params should be ::

      model_dir = "./MobileNetV2"

      input_tensor = 'image'

      target_tensor = 'save_infer_model/scale_0'

      x_shape = [1, 3, 224, 224]

- When choosing model EfficientNet, the params should be ::

      model_dir = "./EfficientNet"

      input_tensor = 'image'

      target_tensor = 'save_infer_model/scale_0'

      x_shape = [1, 3, 224, 224]

- When choosing model FaceDet, the params should be ::

      model_dir = "./FaceDet"

      input_tensor = 'image'

      target_tensor = 'save_infer_model/scale_0'

      x_shape = [1, 3, 240, 320]



.. code-block:: python

    model_dir = "./ResNet50"
    input_tensor = 'inputs'
    target_tensor = 'save_infer_model/scale_0.tmp_1'
    x_shape = [1, 3, 224, 224]








**Set the target backend**
------------------------------
Now CINN only supports two backends: X86 and CUDA.

- For CUDA backends, set ``target = DefaultNVGPUTarget()``

- For X86 backends, set ``target = DefaultHostTarget()``



.. code-block:: python

    if os.path.exists("is_cuda"):
        target = DefaultNVGPUTarget()
    else:
        target = DefaultHostTarget()








**Load Model to CINN**
-------------------------
Load the paddle model and transform it into CINN IR.

* :code:`target` is the backend to execute model on.

* :code:`model_dir` is the path where the paddle model is stored.

* :code:`params_combined` implies whether the params of paddle model is stored in one file.




.. code-block:: python

    params_combined = True
    computation = Computation.compile_paddle_model(
        target, model_dir, [input_tensor], [x_shape], params_combined)








**Get input tensor and set input data**
-----------------------------------------
Here we use random data as input. In practical applications,
please replace it with real data according to your needs.



.. code-block:: python

    a_t = computation.get_tensor(input_tensor)
    x_data = np.random.random(x_shape).astype("float32")
    a_t.from_numpy(x_data, target)








Here we set the output tensor's data to zero before running the model.


.. code-block:: python

    out = computation.get_tensor(target_tensor)
    out.from_numpy(np.zeros(out.shape(), dtype='float32'), target)








**Execute Model**
-------------------------
Execute the model and get output tensor's data.
:code:`out` is the data of output tensor we want.


.. code-block:: python

    computation.execute()
    res_cinn = out.numpy(target)
    print("CINN Execution Done!")





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

 Out:

 .. code-block:: none

    CINN Execution Done!




**Use Paddle to Verify Correctness**
-------------------------
Now we run the model by paddle and check if the 2 results are identical.


.. code-block:: python

    config = fluid.core.AnalysisConfig(model_dir + '/__model__',
                                       model_dir + '/params')
    config.disable_gpu()
    config.switch_ir_optim(False)
    paddle_predictor = fluid.core.create_paddle_predictor(config)
    data = fluid.core.PaddleTensor(x_data)
    paddle_out = paddle_predictor.run([data])
    res_paddle = paddle_out[0].as_ndarray()
    print("Paddle Execution Done!\n =============================")
    print("Verification result is: ", np.allclose(res_cinn, res_paddle, atol=1e-3))




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

 Out:

 .. code-block:: none

    Paddle Execution Done!
     =============================
    Verification result is:  True





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

   **Total running time of the script:** ( 3 minutes  35.496 seconds)


.. _sphx_glr_download_tutorials_load_paddle_model.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: load_paddle_model.py <load_paddle_model.py>`



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

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


.. only:: html

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

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