Load from ONNX
==============

In this example, we will see step by step how to load a model from ONNX.

You can find the full python script here :download:`lenet_onnx.py</../python/examples/lenet_onnx.py>` with the associated onnx file here :download:`LeNet.onnx</../python/examples/LeNet.onnx>`.

.. figure:: /_static/python_api/examples/LeNet_onnx_full.png
   :alt: LeNet_onnx_full.

Loading an ONNX
---------------

Before loading the ONNX file, we need to create an :py:class:`n2d2.database.MNIST` and :py:class:`n2d2.provider.DataProvider` objects.

.. code-block::

    # Change default model to run with CUDA 
    n2d2.global_variables.default_model = "Frame_CUDA"
    # Change cuda device (default 0)
    n2d2.global_variables.cuda_device = args.device
    nb_epochs = args.epochs
    batch_size = 54

    print("\n### Create database ###")
    database = n2d2.database.MNIST(data_path=args.data_path, validation=0.1)

    print("\n### Create Provider ###")
    provider = n2d2.provider.DataProvider(database, [28, 28, 1], batch_size=batch_size)
    provider.add_transformation(n2d2.transform.Rescale(width=28, height=28))
    print(provider)

Once this is done, you can load your ONNX file with one line :

.. code-block::

    print("\n### Loading Model ###")
    model = n2d2.cells.DeepNetCell.load_from_ONNX(provider, args.onnx)
    print(model)

You should observe a verbose output of the loaded ONNX model :

.. testoutput::

    'DeepNetCell_0' DeepNetCell(
            (0): '9' Conv(Frame_CUDA<float>)(nb_inputs=1, nb_outputs=4, kernel_dims=[3, 3], sub_sample_dims=[1, 1], stride_dims=[1, 1], padding_dims=[0, 0], dilation_dims=[1, 1] | back_propagate=True, no_bias=False, outputs_remap=, weights_export_flip=False, weights_export_format=OCHW, activation=Rectifier(clipping=0.0, leak_slope=0.0, quantizer=None), weights_solver=SGD(clamping=, decay=0.0, iteration_size=1, learning_rate=0.01, learning_rate_decay=0.1, learning_rate_policy=None, learning_rate_step_size=1, max_iterations=0, min_decay=0.0, momentum=0.0, polyak_momentum=True, power=0.0, warm_up_duration=0, warm_up_lr_frac=0.25), bias_solver=SGD(clamping=, decay=0.0, iteration_size=1, learning_rate=0.01, learning_rate_decay=0.1, learning_rate_policy=None, learning_rate_step_size=1, max_iterations=0, min_decay=0.0, momentum=0.0, polyak_momentum=True, power=0.0, warm_up_duration=0, warm_up_lr_frac=0.25), weights_filler=Normal(mean=0.0, std_dev=0.05), bias_filler=Normal(mean=0.0, std_dev=0.05), quantizer=None)
            (1): '11' Conv(Frame_CUDA<float>)(nb_inputs=4, nb_outputs=4, kernel_dims=[3, 3], sub_sample_dims=[1, 1], stride_dims=[1, 1], padding_dims=[0, 0], dilation_dims=[1, 1] | back_propagate=True, no_bias=False, outputs_remap=, weights_export_flip=False, weights_export_format=OCHW, activation=Rectifier(clipping=0.0, leak_slope=0.0, quantizer=None), weights_solver=SGD(clamping=, decay=0.0, iteration_size=1, learning_rate=0.01, learning_rate_decay=0.1, learning_rate_policy=None, learning_rate_step_size=1, max_iterations=0, min_decay=0.0, momentum=0.0, polyak_momentum=True, power=0.0, warm_up_duration=0, warm_up_lr_frac=0.25), bias_solver=SGD(clamping=, decay=0.0, iteration_size=1, learning_rate=0.01, learning_rate_decay=0.1, learning_rate_policy=None, learning_rate_step_size=1, max_iterations=0, min_decay=0.0, momentum=0.0, polyak_momentum=True, power=0.0, warm_up_duration=0, warm_up_lr_frac=0.25), weights_filler=Normal(mean=0.0, std_dev=0.05), bias_filler=Normal(mean=0.0, std_dev=0.05), quantizer=None)(['9'])
            (2): '13' Pool(Frame_CUDA<float>)(pool_dims=[2, 2], stride_dims=[2, 2], padding_dims=[0, 0], pooling=Pooling.Max | activation=None)(['11'])
            (3): '15' Fc(Frame_CUDA<float>)(nb_inputs=576, nb_outputs=128 | back_propagate=True, no_bias=False, normalize=False, outputs_remap=, weights_export_format=OC, activation=Rectifier(clipping=0.0, leak_slope=0.0, quantizer=None), weights_solver=SGD(clamping=, decay=0.0, iteration_size=1, learning_rate=0.01, learning_rate_decay=0.1, learning_rate_policy=None, learning_rate_step_size=1, max_iterations=0, min_decay=0.0, momentum=0.0, polyak_momentum=True, power=0.0, warm_up_duration=0, warm_up_lr_frac=0.25), bias_solver=SGD(clamping=, decay=0.0, iteration_size=1, learning_rate=0.01, learning_rate_decay=0.1, learning_rate_policy=None, learning_rate_step_size=1, max_iterations=0, min_decay=0.0, momentum=0.0, polyak_momentum=True, power=0.0, warm_up_duration=0, warm_up_lr_frac=0.25), weights_filler=Normal(mean=0.0, std_dev=0.05), bias_filler=Normal(mean=0.0, std_dev=0.05), quantizer=None)(['13'])
            (4): '17' Fc(Frame_CUDA<float>)(nb_inputs=128, nb_outputs=10 | back_propagate=True, no_bias=False, normalize=False, outputs_remap=, weights_export_format=OC, activation=Linear(clipping=0.0, quantizer=None), weights_solver=SGD(clamping=, decay=0.0, iteration_size=1, learning_rate=0.01, learning_rate_decay=0.1, learning_rate_policy=None, learning_rate_step_size=1, max_iterations=0, min_decay=0.0, momentum=0.0, polyak_momentum=True, power=0.0, warm_up_duration=0, warm_up_lr_frac=0.25), bias_solver=SGD(clamping=, decay=0.0, iteration_size=1, learning_rate=0.01, learning_rate_decay=0.1, learning_rate_policy=None, learning_rate_step_size=1, max_iterations=0, min_decay=0.0, momentum=0.0, polyak_momentum=True, power=0.0, warm_up_duration=0, warm_up_lr_frac=0.25), weights_filler=Normal(mean=0.0, std_dev=0.05), bias_filler=Normal(mean=0.0, std_dev=0.05), quantizer=None)(['15'])
            (5): '18' Softmax(Frame_CUDA<float>)(with_loss=True, group_size=0 | activation=None)(['17'])
    )

The model has been exported successfully !

Training and exporting the model
--------------------------------

You can now do what you want with your imported model, like training it :

.. code-block::

    model.fit(learn_epoch=nb_epochs, valid_metric='Accuracy')
    model.run_test()

And even exporting it to CPP in int 8 !

.. warning::

    Don't forget to remove the softmax layer first because N2D2 does not export this layer for the CPP export.

.. code-block::

    model.remove("18") # removing Softmax layer before export !
    n2d2.export_cpp(model, nb_bits=8, calibration=1)