scvi.dataloaders.DataSplitter.test_dataloader

DataSplitter.test_dataloader()[source]

Implement one or multiple PyTorch DataLoaders for testing.

The dataloader you return will not be called every epoch unless you set :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to True.

For data processing use the following pattern:

However, the above are only necessary for distributed processing.

Warning

do not assign state in prepare_data

Note

Lightning adds the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

Returns

Single or multiple PyTorch DataLoaders.

Example:

def test_dataloader(self):
    transform = transforms.Compose([transforms.ToTensor(),
                                    transforms.Normalize((0.5,), (1.0,))])
    dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,
                    download=True)
    loader = torch.utils.data.DataLoader(
        dataset=dataset,
        batch_size=self.batch_size,
        shuffle=False
    )

    return loader

# can also return multiple dataloaders
def test_dataloader(self):
    return [loader_a, loader_b, ..., loader_n]

Note

If you don’t need a test dataset and a test_step(), you don’t need to implement this method.

Note

In the case where you return multiple test dataloaders, the test_step() will have an argument dataloader_idx which matches the order here.