Fix mypy errors attributed to pytorch_lightning.demos.boring_classes (#14201)

Co-authored-by: Justus Schock <[email protected]>
Co-authored-by: otaj <[email protected]>

Author: 3 people

pyproject.toml

@@ -51,7 +51,6 @@ warn_no_return = "False"
 module = [
     "pytorch_lightning.callbacks.progress.rich_progress",
     "pytorch_lightning.core.datamodule",
-    "pytorch_lightning.demos.boring_classes",
     "pytorch_lightning.demos.mnist_datamodule",
     "pytorch_lightning.profilers.base",
     "pytorch_lightning.profilers.pytorch",

src/pytorch_lightning/demos/boring_classes.py

@@ -11,22 +11,27 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Optional
+from typing import cast, Dict, Iterator, List, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from torch import Tensor
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import _LRScheduler
 from torch.utils.data import DataLoader, Dataset, IterableDataset, Subset
 
 from pytorch_lightning import LightningDataModule, LightningModule
+from pytorch_lightning.core.optimizer import LightningOptimizer
+from pytorch_lightning.utilities.types import EPOCH_OUTPUT, STEP_OUTPUT
 
 
 class RandomDictDataset(Dataset):
     def __init__(self, size: int, length: int):
         self.len = length
         self.data = torch.randn(length, size)
 
-    def __getitem__(self, index):
+    def __getitem__(self, index: int) -> Dict[str, Tensor]:
         a = self.data[index]
         b = a + 2
         return {"a": a, "b": b}
@@ -40,7 +45,7 @@ def __init__(self, size: int, length: int):
         self.len = length
         self.data = torch.randn(length, size)
 
-    def __getitem__(self, index):
+    def __getitem__(self, index: int) -> Tensor:
         return self.data[index]
 
     def __len__(self) -> int:
@@ -52,7 +57,7 @@ def __init__(self, size: int, count: int):
         self.count = count
         self.size = size
 
-    def __iter__(self):
+    def __iter__(self) -> Iterator[Tensor]:
         for _ in range(self.count):
             yield torch.randn(self.size)
 
@@ -62,16 +67,16 @@ def __init__(self, size: int, count: int):
         self.count = count
         self.size = size
 
-    def __iter__(self):
+    def __iter__(self) -> Iterator[Tensor]:
         for _ in range(len(self)):
             yield torch.randn(self.size)
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self.count
 
 
 class BoringModel(LightningModule):
-    def __init__(self):
+    def __init__(self) -> None:
         """Testing PL Module.
 
         Use as follows:
@@ -90,60 +95,63 @@ def training_step(...):
         super().__init__()
         self.layer = torch.nn.Linear(32, 2)
 
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Tensor:  # type: ignore[override]
         return self.layer(x)
 
-    def loss(self, batch, preds):
+    def loss(self, batch: Tensor, preds: Tensor) -> Tensor:
         # An arbitrary loss to have a loss that updates the model weights during `Trainer.fit` calls
         return torch.nn.functional.mse_loss(preds, torch.ones_like(preds))
 
-    def step(self, x):
+    def step(self, x: Tensor) -> Tensor:
         x = self(x)
         out = torch.nn.functional.mse_loss(x, torch.ones_like(x))
         return out
 
-    def training_step(self, batch, batch_idx):
+    def training_step(self, batch: Tensor, batch_idx: int) -> STEP_OUTPUT:  # type: ignore[override]
         output = self(batch)
         loss = self.loss(batch, output)
         return {"loss": loss}
 
-    def training_step_end(self, training_step_outputs):
+    def training_step_end(self, training_step_outputs: STEP_OUTPUT) -> STEP_OUTPUT:
         return training_step_outputs
 
-    def training_epoch_end(self, outputs) -> None:
+    def training_epoch_end(self, outputs: EPOCH_OUTPUT) -> None:
+        outputs = cast(List[Dict[str, Tensor]], outputs)
         torch.stack([x["loss"] for x in outputs]).mean()
 
-    def validation_step(self, batch, batch_idx):
+    def validation_step(self, batch: Tensor, batch_idx: int) -> Optional[STEP_OUTPUT]:  # type: ignore[override]
         output = self(batch)
         loss = self.loss(batch, output)
         return {"x": loss}
 
-    def validation_epoch_end(self, outputs) -> None:
+    def validation_epoch_end(self, outputs: Union[EPOCH_OUTPUT, List[EPOCH_OUTPUT]]) -> None:
+        outputs = cast(List[Dict[str, Tensor]], outputs)
         torch.stack([x["x"] for x in outputs]).mean()
 
-    def test_step(self, batch, batch_idx):
+    def test_step(self, batch: Tensor, batch_idx: int) -> Optional[STEP_OUTPUT]:  # type: ignore[override]
         output = self(batch)
         loss = self.loss(batch, output)
         return {"y": loss}
 
-    def test_epoch_end(self, outputs) -> None:
+    def test_epoch_end(self, outputs: Union[EPOCH_OUTPUT, List[EPOCH_OUTPUT]]) -> None:
+        outputs = cast(List[Dict[str, Tensor]], outputs)
         torch.stack([x["y"] for x in outputs]).mean()
 
-    def configure_optimizers(self):
+    def configure_optimizers(self) -> Tuple[List[torch.optim.Optimizer], List[_LRScheduler]]:
         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]
 
-    def train_dataloader(self):
+    def train_dataloader(self) -> DataLoader:
         return DataLoader(RandomDataset(32, 64))
 
-    def val_dataloader(self):
+    def val_dataloader(self) -> DataLoader:
         return DataLoader(RandomDataset(32, 64))
 
-    def test_dataloader(self):
+    def test_dataloader(self) -> DataLoader:
         return DataLoader(RandomDataset(32, 64))
 
-    def predict_dataloader(self):
+    def predict_dataloader(self) -> DataLoader:
         return DataLoader(RandomDataset(32, 64))
 
 
@@ -155,7 +163,7 @@ def __init__(self, data_dir: str = "./"):
         self.checkpoint_state: Optional[str] = None
         self.random_full = RandomDataset(32, 64 * 4)
 
-    def setup(self, stage: Optional[str] = None):
+    def setup(self, stage: Optional[str] = None) -> None:
         if stage == "fit" or stage is None:
             self.random_train = Subset(self.random_full, indices=range(64))
 
@@ -168,26 +176,27 @@ def setup(self, stage: Optional[str] = None):
         if stage == "predict" or stage is None:
             self.random_predict = Subset(self.random_full, indices=range(64 * 3, 64 * 4))
 
-    def train_dataloader(self):
+    def train_dataloader(self) -> DataLoader:
         return DataLoader(self.random_train)
 
-    def val_dataloader(self):
+    def val_dataloader(self) -> DataLoader:
         return DataLoader(self.random_val)
 
-    def test_dataloader(self):
+    def test_dataloader(self) -> DataLoader:
         return DataLoader(self.random_test)
 
-    def predict_dataloader(self):
+    def predict_dataloader(self) -> DataLoader:
         return DataLoader(self.random_predict)
 
 
 class ManualOptimBoringModel(BoringModel):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__()
         self.automatic_optimization = False
 
-    def training_step(self, batch, batch_idx):
+    def training_step(self, batch: Tensor, batch_idx: int) -> STEP_OUTPUT:  # type: ignore[override]
         opt = self.optimizers()
+        assert isinstance(opt, (Optimizer, LightningOptimizer))
         output = self(batch)
         loss = self.loss(batch, output)
         opt.zero_grad()
@@ -202,21 +211,21 @@ def __init__(self, out_dim: int = 10, learning_rate: float = 0.02):
         self.l1 = torch.nn.Linear(32, out_dim)
         self.learning_rate = learning_rate
 
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Tensor:  # type: ignore[override]
         return torch.relu(self.l1(x.view(x.size(0), -1)))
 
-    def training_step(self, batch, batch_nb):
+    def training_step(self, batch: Tensor, batch_nb: int) -> STEP_OUTPUT:  # type: ignore[override]
         x = batch
         x = self(x)
         loss = x.sum()
         return loss
 
-    def configure_optimizers(self):
+    def configure_optimizers(self) -> torch.optim.Optimizer:
         return torch.optim.Adam(self.parameters(), lr=self.learning_rate)
 
 
 class Net(nn.Module):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__()
         self.conv1 = nn.Conv2d(1, 32, 3, 1)
         self.conv2 = nn.Conv2d(32, 64, 3, 1)
@@ -225,7 +234,7 @@ def __init__(self):
         self.fc1 = nn.Linear(9216, 128)
         self.fc2 = nn.Linear(128, 10)
 
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Tensor:
         x = self.conv1(x)
         x = F.relu(x)
         x = self.conv2(x)