Lightning forces the user to run the test set separately to make sure it isn't evaluated by mistake. Testing is performed using the trainer
object's .test()
method.
pytorch_lightning.trainer.Trainer.test
To run the test set after training completes, use this method.
# run full training
trainer.fit(model)
# (1) load the best checkpoint automatically (lightning tracks this for you)
trainer.test()
# (2) don't load a checkpoint, instead use the model with the latest weights
trainer.test(ckpt_path=None)
# (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)
You can run the test set on multiple models using the same trainer instance.
model1 = LitModel()
model2 = GANModel()
trainer = Trainer()
trainer.test(model1)
trainer.test(model2)
To run the test set on a pre-trained model, use this method.
model = MyLightningModule.load_from_checkpoint(
checkpoint_path="/path/to/pytorch_checkpoint.ckpt",
hparams_file="/path/to/test_tube/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...)
You can still run inference on a test set even if the test_dataloader method hasn't been defined within your 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.
# 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 datamodules <../extensions/datamodules>
that have overridden the datamodule-test-dataloader-label
method.
class MyDataModule(pl.LightningDataModule):
...
def test_dataloader(self):
return DataLoader(...)
# setup your datamodule
dm = MyDataModule(...)
# test (pass in datamodule)
trainer.test(datamodule=dm)