Calling trainer.test() when using fast_dev_run throws confusing error
🐛 Bug
Calling trainer.test() when using fast_dev_run throws confusing error:
Traceback (most recent call last):                                                                                                                                                                 
  File "main.py", line 89, in <module>
    trainer.test(test_dataloaders=test)
  File "/home/ash/miniconda3/envs/tmp/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 916, in test
    results = self.__test_using_best_weights(ckpt_path, test_dataloaders)
  File "/home/ash/miniconda3/envs/tmp/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 927, in __test_using_best_weights
    raise MisconfigurationException(
pytorch_lightning.utilities.exceptions.MisconfigurationException: ckpt_path is "best", but ModelCheckpoint is not configured to save the best model.
Please reproduce using the BoringModel
from pytorch_lightning import LightningModule
import torch
from torch.utils.data import DataLoader, Dataset
import pytorch_lightning as pl


class RandomDataset(Dataset):
    def __init__(self, size, num_samples):
        self.len = num_samples
        self.data = torch.randn(num_samples, size)

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return self.len


class BoringModel(LightningModule):

    def __init__(self):
        super().__init__()
        self.layer = torch.nn.Linear(32, 2)

    def forward(self, x):
        return self.layer(x)

    def loss(self, batch, prediction):
        return torch.nn.functional.mse_loss(prediction, torch.ones_like(prediction))

    def training_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        return {"loss": loss}

    def training_step_end(self, training_step_outputs):
        return training_step_outputs

    def training_epoch_end(self, outputs) -> None:
        torch.stack([x["loss"] for x in outputs]).mean()

    def validation_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        return {"x": loss}

    def validation_epoch_end(self, outputs) -> None:
        torch.stack([x['x'] for x in outputs]).mean()

    def test_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        self.log('fake_test_acc', loss)
        return {"y": loss}

    def test_epoch_end(self, outputs) -> None:
        torch.stack([x["y"] for x in outputs]).mean()

    def configure_optimizers(self):
        optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
        lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
        return [optimizer], [lr_scheduler]


num_samples = 10000

train = RandomDataset(32, num_samples)
train = DataLoader(train, batch_size=32)

val = RandomDataset(32, num_samples)
val = DataLoader(val, batch_size=32)

test = RandomDataset(32, num_samples)
test = DataLoader(test, batch_size=32)

model = BoringModel()

trainer = pl.Trainer(
    fast_dev_run=True
)

trainer.fit(model, train, val)

trainer.test(test_dataloaders=test)