Model Evaluators
================

A model evaluator is for training and validating each generated model. They are necessary to evaluate the performance of new explored models.

.. _functional-evaluator:

Customize Evaluator with Any Function
-------------------------------------

The simplest way to customize a new evaluator is with functional APIs, which is very easy when training code is already available. Users only need to write a fit function that wraps everything, which usually includes training, validating and testing of a single model. This function takes one positional arguments (``model_cls``) and possible keyword arguments. The keyword arguments (other than ``model_cls``) are fed to FunctionEvaluator as its initialization parameters (note that they will be :doc:`serialized <./serialization>`). In this way, users get everything under their control, but expose less information to the framework and as a result, further optimizations like :ref:`CGO <cgo-execution-engine>` might be not feasible. An example is as belows:

.. code-block:: python

    from nni.retiarii.evaluator import FunctionalEvaluator
    from nni.retiarii.experiment.pytorch import RetiariiExperiment

    def fit(model_cls, dataloader):
        model = model_cls()
        train(model, dataloader)
        acc = test(model, dataloader)
        nni.report_final_result(acc)

    # The dataloader will be serialized, thus ``nni.trace`` is needed here.
    # See serialization tutorial for more details.
    evaluator = FunctionalEvaluator(fit, dataloader=nni.trace(DataLoader)(foo, bar))
    experiment = RetiariiExperiment(base_model, evaluator, mutators, strategy)

.. tip::

    When using customized evaluators, if you want to visualize models, you need to export your model and save it into ``$NNI_OUTPUT_DIR/model.onnx`` in your evaluator. An example here:

    .. code-block:: python

        def fit(model_cls):
            model = model_cls()
            onnx_path = Path(os.environ.get('NNI_OUTPUT_DIR', '.')) / 'model.onnx'
            onnx_path.parent.mkdir(exist_ok=True)
            dummy_input = torch.randn(10, 3, 224, 224)
            torch.onnx.export(model, dummy_input, onnx_path)
            # the rest of training code here

    If the conversion is successful, the model will be able to be visualized with powerful tools `Netron <https://netron.app/>`__.

Evaluators with PyTorch-Lightning
---------------------------------

Use Built-in Evaluators
^^^^^^^^^^^^^^^^^^^^^^^

NNI provides some commonly used model evaluators for users' convenience. These evaluators are built upon the awesome library PyTorch-Lightning.

We recommend to read the `serialization tutorial <./Serialization.rst>`__ before using these evaluators. A few notes to summarize the tutorial:

1. ``pl.DataLoader`` should be used in place of ``torch.utils.data.DataLoader``.
2. The datasets used in data-loader should be decorated with ``nni.trace`` recursively.

For example,

.. code-block:: python

  import nni.retiarii.evaluator.pytorch.lightning as pl
  from torchvision import transforms

  transform = nni.trace(transforms.Compose, [nni.trace(transforms.ToTensor()), nni.trace(transforms.Normalize, (0.1307,), (0.3081,))])
  train_dataset = nni.trace(MNIST, root='data/mnist', train=True, download=True, transform=transform)
  test_dataset = nni.trace(MNIST, root='data/mnist', train=False, download=True, transform=transform)

  # pl.DataLoader and pl.Classification is already traced and supports serialization.
  evaluator = pl.Classification(train_dataloader=pl.DataLoader(train_dataset, batch_size=100),
                                val_dataloaders=pl.DataLoader(test_dataset, batch_size=100),
                                max_epochs=10)

Customize Evaluator with PyTorch-Lightning
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Another approach is to write training code in PyTorch-Lightning style, that is, to write a LightningModule that defines all elements needed for training (e.g., loss function, optimizer) and to define a trainer that takes (optional) dataloaders to execute the training. Before that, please read the `document of PyTorch-lightning <https://pytorch-lightning.readthedocs.io/>`__ to learn the basic concepts and components provided by PyTorch-lightning.

In practice, writing a new training module in Retiarii should inherit ``nni.retiarii.evaluator.pytorch.lightning.LightningModule``, which has a ``set_model`` that will be called after ``__init__`` to save the candidate model (generated by strategy) as ``self.model``. The rest of the process (like ``training_step``) should be the same as writing any other lightning module. Evaluators should also communicate with strategies via two API calls (``nni.report_intermediate_result`` for periodical metrics and ``nni.report_final_result`` for final metrics), added in ``on_validation_epoch_end`` and ``teardown`` respectively. 

An example is as follows:

.. code-block:: python

    from nni.retiarii.evaluator.pytorch.lightning import LightningModule  # please import this one

    @nni.trace
    class AutoEncoder(LightningModule):
        def __init__(self):
            super().__init__()
            self.decoder = nn.Sequential(
                nn.Linear(3, 64),
                nn.ReLU(),
                nn.Linear(64, 28*28)
            )

        def forward(self, x):
            embedding = self.model(x)  # let's search for encoder
            return embedding

        def training_step(self, batch, batch_idx):
            # training_step defined the train loop.
            # It is independent of forward
            x, y = batch
            x = x.view(x.size(0), -1)
            z = self.model(x)  # model is the one that is searched for
            x_hat = self.decoder(z)
            loss = F.mse_loss(x_hat, x)
            # Logging to TensorBoard by default
            self.log('train_loss', loss)
            return loss

        def validation_step(self, batch, batch_idx):
            x, y = batch
            x = x.view(x.size(0), -1)
            z = self.model(x)
            x_hat = self.decoder(z)
            loss = F.mse_loss(x_hat, x)
            self.log('val_loss', loss)

        def configure_optimizers(self):
            optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
            return optimizer

        def on_validation_epoch_end(self):
            nni.report_intermediate_result(self.trainer.callback_metrics['val_loss'].item())

        def teardown(self, stage):
            if stage == 'fit':
                nni.report_final_result(self.trainer.callback_metrics['val_loss'].item())

Then, users need to wrap everything (including LightningModule, trainer and dataloaders) into a ``Lightning`` object, and pass this object into a Retiarii experiment.

.. code-block:: python

    import nni.retiarii.evaluator.pytorch.lightning as pl
    from nni.retiarii.experiment.pytorch import RetiariiExperiment

    lightning = pl.Lightning(AutoEncoder(),
                             pl.Trainer(max_epochs=10),
                             train_dataloader=pl.DataLoader(train_dataset, batch_size=100),
                             val_dataloaders=pl.DataLoader(test_dataset, batch_size=100))
    experiment = RetiariiExperiment(base_model, lightning, mutators, strategy)

References
----------

FunctionalEvaluator
^^^^^^^^^^^^^^^^^^^

..  autoclass:: nni.retiarii.evaluator.FunctionalEvaluator
    :members:
    :noindex:

.. _classification-evaluator:

Classification
^^^^^^^^^^^^^^

..  autoclass:: nni.retiarii.evaluator.pytorch.lightning.Classification
    :members:
    :noindex:

.. _regression-evaluator:

Regression
^^^^^^^^^^

..  autoclass:: nni.retiarii.evaluator.pytorch.lightning.Regression
    :members:
    :noindex:

Lightning
^^^^^^^^^

..  autoclass:: nni.retiarii.evaluator.pytorch.lightning.Lightning
    :members:
    :noindex:

