.. _test_set:

:orphan:

######################################## Validate and test a model (intermediate) ########################################

During and after training we need a way to evaluate our models to make sure they are not overfitting while training and generalize well on unseen or real-world data. There are generally 2 stages of evaluation: validation and testing. To some degree they serve the same purpose, to make sure models works on real data but they have some practical differences.

Validation is usually done during training, traditionally after each training epoch. It can be used for hyperparameter optimization or tracking model performance during training. It's a part of the training process.

Testing is usually done once we are satisfied with the training and only with the best model selected from the validation metrics.

Let's see how these can be performed with Lightning.

Testing

Lightning allows the user to test their models with any compatible test dataloaders. This can be done before/after training and is completely agnostic to :meth:~lightning.pytorch.trainer.trainer.Trainer.fit call. The logic used here is defined under :meth:~lightning.pytorch.core.LightningModule.test_step.

Testing is performed using the Trainer object's .test() method.

.. automethod:: lightning.pytorch.trainer.Trainer.test :noindex:

Test after Fit

To run the test set after training completes, use this method.

.. code-block:: python

# run full training
trainer.fit(model)

# (1) load the best checkpoint automatically (lightning tracks this for you during .fit())
trainer.test(ckpt_path="best")

# (2) load the last available checkpoint (only works if `ModelCheckpoint(save_last=True)`)
trainer.test(ckpt_path="last")

# (3) test using a specific checkpoint
trainer.test(ckpt_path="/path/to/my_checkpoint.ckpt")

# (4) test with an explicit model (will use this model and not load a checkpoint)
trainer.test(model)

.. warning::

It is recommended to test with ``Trainer(devices=1)`` since distributed strategies such as DDP
use :class:`~torch.utils.data.distributed.DistributedSampler` internally, which replicates some samples to
make sure all devices have same batch size in case of uneven inputs. This is helpful to make sure
benchmarking for research papers is done the right way.

Test Multiple Models

You can run the test set on multiple models using the same trainer instance.

.. code-block:: python

model1 = LitModel()
model2 = GANModel()

trainer = Trainer()
trainer.test(model1)
trainer.test(model2)

Test Pre-Trained Model

To run the test set on a pre-trained model, use this method.

.. code-block:: python

model = MyLightningModule.load_from_checkpoint(
    checkpoint_path="/path/to/pytorch_checkpoint.ckpt",
    hparams_file="/path/to/experiment/version/hparams.yaml",
    map_location=None,
)

# init trainer with whatever options
trainer = Trainer(...)

# test (pass in the model)
trainer.test(model)

In this case, the options you pass to trainer will be used when running the test set (ie: 16-bit, dp, ddp, etc...)

Test with Additional DataLoaders

You can still run inference on a test dataset even if the :meth:~lightning.pytorch.core.hooks.DataHooks.test_dataloader method hasn't been defined within your :doc:lightning module <../common/lightning_module> instance. This would be the case when your test data is not available at the time your model was declared.

.. code-block:: python

# setup your data loader
test_dataloader = DataLoader(...)

# test (pass in the loader)
trainer.test(dataloaders=test_dataloader)

You can either pass in a single dataloader or a list of them. This optional named parameter can be used in conjunction with any of the above use cases. Additionally, you can also pass in an :doc:datamodules <../data/datamodule> that have overridden the :ref:datamodule_test_dataloader_label method.

.. code-block:: python

class MyDataModule(L.LightningDataModule):
    ...

    def test_dataloader(self):
        return DataLoader(...)


# setup your datamodule
dm = MyDataModule(...)

# test (pass in datamodule)
trainer.test(datamodule=dm)

Test with Multiple DataLoaders

When you need to evaluate your model on multiple test datasets simultaneously (e.g., different domains, conditions, or evaluation scenarios), PyTorch Lightning supports multiple test dataloaders out of the box.

To use multiple test dataloaders, simply return a list of dataloaders from your test_dataloader() method:

.. code-block:: python

class LitModel(L.LightningModule):
    def test_dataloader(self):
        return [
            DataLoader(clean_test_dataset, batch_size=32),
            DataLoader(noisy_test_dataset, batch_size=32),
            DataLoader(adversarial_test_dataset, batch_size=32),
        ]

When using multiple test dataloaders, your test_step method must include a dataloader_idx parameter:

.. code-block:: python

def test_step(self, batch, batch_idx, dataloader_idx: int = 0):
    x, y = batch
    y_hat = self(x)
    loss = F.cross_entropy(y_hat, y)

    # Use dataloader_idx to handle different test scenarios
    return {'test_loss': loss}

Logging Metrics Per Dataloader


Lightning provides automatic support for logging metrics per dataloader:

.. code-block:: python

    def test_step(self, batch, batch_idx, dataloader_idx: int = 0):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)
        acc = (y_hat.argmax(dim=1) == y).float().mean()

        # Lightning automatically adds "/dataloader_idx_X" suffix
        self.log('test_loss', loss, add_dataloader_idx=True)
        self.log('test_acc', acc, add_dataloader_idx=True)

        return loss

This will create metrics like ``test_loss/dataloader_idx_0``, ``test_loss/dataloader_idx_1``, etc.

For more meaningful metric names, you can use custom naming where you need to make sure that individual names are
unique across dataloaders.

.. code-block:: python

    def test_step(self, batch, batch_idx, dataloader_idx: int = 0):
        # Define meaningful names for each dataloader
        dataloader_names = {0: "clean", 1: "noisy", 2: "adversarial"}
        dataset_name = dataloader_names.get(dataloader_idx, f"dataset_{dataloader_idx}")

        # Log with custom names
        self.log(f'test_loss_{dataset_name}', loss, add_dataloader_idx=False)
        self.log(f'test_acc_{dataset_name}', acc, add_dataloader_idx=False)

Processing Entire Datasets Per Dataloader

To perform calculations on the entire test dataset for each dataloader (e.g., computing overall metrics, creating visualizations), accumulate results during test_step and process them in on_test_epoch_end:

.. code-block:: python

class LitModel(L.LightningModule):
    def __init__(self):
        super().__init__()
        # Store outputs per dataloader
        self.test_outputs = {}

    def test_step(self, batch, batch_idx, dataloader_idx: int = 0):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)

        # Initialize and store results
        if dataloader_idx not in self.test_outputs:
            self.test_outputs[dataloader_idx] = {'predictions': [], 'targets': []}
        self.test_outputs[dataloader_idx]['predictions'].append(y_hat)
        self.test_outputs[dataloader_idx]['targets'].append(y)
        return loss

    def on_test_epoch_end(self):
        for dataloader_idx, outputs in self.test_outputs.items():
            # Concatenate all predictions and targets for this dataloader
            all_predictions = torch.cat(outputs['predictions'], dim=0)
            all_targets = torch.cat(outputs['targets'], dim=0)

            # Calculate metrics on the entire dataset, log and create visualizations
            overall_accuracy = (all_predictions.argmax(dim=1) == all_targets).float().mean()
            self.log(f'test_overall_acc_dataloader_{dataloader_idx}', overall_accuracy)
            self._save_results(all_predictions, all_targets, dataloader_idx)

        self.test_outputs.clear()

.. note:: When using multiple test dataloaders, trainer.test() returns a list of results, one for each dataloader:

.. code-block:: python

    results = trainer.test(model)
    print(f"Results from {len(results)} test dataloaders:")
    for i, result in enumerate(results):
        print(f"Dataloader {i}: {result}")

Validation

Lightning allows the user to validate their models with any compatible val dataloaders. This can be done before/after training. The logic associated to the validation is defined within the :meth:~lightning.pytorch.core.LightningModule.validation_step.

Apart from this .validate has same API as .test, but would rely respectively on :meth:~lightning.pytorch.core.LightningModule.validation_step and :meth:~lightning.pytorch.core.LightningModule.test_step.

.. note:: .validate method uses the same validation logic being used under validation happening within :meth:~lightning.pytorch.trainer.trainer.Trainer.fit call.

.. warning::

When using ``trainer.validate()``, it is recommended to use ``Trainer(devices=1)`` since distributed strategies such as DDP
uses :class:`~torch.utils.data.distributed.DistributedSampler` internally, which replicates some samples to
make sure all devices have same batch size in case of uneven inputs. This is helpful to make sure
benchmarking for research papers is done the right way.

.. automethod:: lightning.pytorch.trainer.Trainer.validate :noindex: