Fix mypy typing for `utilities.apply_func` (#8781)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jirka <jirka.borovec@seznam.cz>
Co-authored-by: Carlos Mocholi <carlossmocholi@gmail.com>

pyproject.toml

@@ -65,6 +65,7 @@ module = [
     "pytorch_lightning.loops.closure",
     "pytorch_lightning.trainer.evaluation_loop",
     "pytorch_lightning.trainer.connectors.logger_connector",
+    "pytorch_lightning.utilities.apply_func",
     "pytorch_lightning.utilities.argparse",
     "pytorch_lightning.utilities.cli",
     "pytorch_lightning.utilities.cloud_io",

pytorch_lightning/utilities/apply_func.py

@@ -18,7 +18,7 @@ from collections import OrderedDict
 from collections.abc import Mapping, Sequence
 from copy import copy
 from functools import partial
-from typing import Any, Callable, Optional, Union
+from typing import Any, Callable, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -35,19 +35,23 @@ else:
     Batch = type(None)
 
 
-def to_dtype_tensor(value, dtype: torch.dtype = None, device: torch.device = None):
+def to_dtype_tensor(
+    value: Union[int, float, List[Union[int, float]]],
+    dtype: Optional[torch.dtype] = None,
+    device: Union[str, torch.device] = None,
+) -> torch.Tensor:
     if device is None:
         raise MisconfigurationException("device (torch.device) should be provided.")
     return torch.tensor(value, dtype=dtype, device=device)
 
 
-def from_numpy(value, device: torch.device = None):
+def from_numpy(value: np.ndarray, device: Union[str, torch.device] = None) -> torch.Tensor:
     if device is None:
         raise MisconfigurationException("device (torch.device) should be provided.")
     return torch.from_numpy(value).to(device)
 
 
-CONVERSION_DTYPES = [
+CONVERSION_DTYPES: List[Tuple[Any, Callable[[Any], torch.Tensor]]] = [
     # bool -> uint8 as bool -> torch.bool triggers RuntimeError: Unsupported data type for NCCL process group
     (bool, partial(to_dtype_tensor, dtype=torch.uint8)),
     (int, partial(to_dtype_tensor, dtype=torch.int)),
@@ -61,19 +65,19 @@ def _is_namedtuple(obj: object) -> bool:
     return isinstance(obj, tuple) and hasattr(obj, "_asdict") and hasattr(obj, "_fields")
 
 
-def _is_dataclass_instance(obj):
+def _is_dataclass_instance(obj: object) -> bool:
     # https://docs.python.org/3/library/dataclasses.html#module-level-decorators-classes-and-functions
     return dataclasses.is_dataclass(obj) and not isinstance(obj, type)
 
 
 def apply_to_collection(
     data: Any,
-    dtype: Union[type, tuple],
+    dtype: Union[type, Any, Tuple[Union[type, Any]]],
     function: Callable,
-    *args,
-    wrong_dtype: Optional[Union[type, tuple]] = None,
+    *args: Any,
+    wrong_dtype: Optional[Union[type, Tuple[type]]] = None,
     include_none: bool = True,
-    **kwargs
+    **kwargs: Any,
 ) -> Any:
     """
     Recursively applies a function to all elements of a certain dtype.
@@ -121,7 +125,7 @@ def apply_to_collection(
         return elem_type(*out) if is_namedtuple else elem_type(out)
 
     if _is_dataclass_instance(data):
-        out = {}
+        out_dict = {}
         for field in data.__dataclass_fields__:
             v = apply_to_collection(
                 getattr(data, field),
@@ -130,11 +134,11 @@ def apply_to_collection(
                 *args,
                 wrong_dtype=wrong_dtype,
                 include_none=include_none,
-                **kwargs
+                **kwargs,
             )
             if include_none or v is not None:
-                out[field] = v
-        return elem_type(**out)
+                out_dict[field] = v
+        return elem_type(**out_dict)
 
     # data is neither of dtype, nor a collection
     return data
@@ -143,11 +147,11 @@ def apply_to_collection(
 def apply_to_collections(
     data1: Optional[Any],
     data2: Optional[Any],
-    dtype: Union[type, tuple],
+    dtype: Union[type, Any, Tuple[Union[type, Any]]],
     function: Callable,
-    *args,
-    wrong_dtype: Optional[Union[type, tuple]] = None,
-    **kwargs
+    *args: Any,
+    wrong_dtype: Optional[Union[type, Tuple[type]]] = None,
+    **kwargs: Any,
 ) -> Any:
     """
     Zips two collections and applies a function to their items of a certain dtype.
@@ -169,7 +173,9 @@ def apply_to_collections(
         AssertionError:
             If sequence collections have different data sizes.
     """
-    if data1 is None and data2 is not None:
+    if data1 is None:
+        if data2 is None:
+            return
         # in case they were passed reversed
         data1, data2 = data2, None
 
@@ -220,14 +226,14 @@ class TransferableDataType(ABC):
     """
 
     @classmethod
-    def __subclasshook__(cls, subclass):
+    def __subclasshook__(cls, subclass: Any) -> Union[bool, Any]:
         if cls is TransferableDataType:
             to = getattr(subclass, "to", None)
             return callable(to)
         return NotImplemented
 
 
-def move_data_to_device(batch: Any, device: torch.device):
+def move_data_to_device(batch: Any, device: Union[str, torch.device]) -> Any:
     """
     Transfers a collection of data to the given device. Any object that defines a method
     ``to(device)`` will be moved and all other objects in the collection will be left untouched.
@@ -245,7 +251,7 @@ def move_data_to_device(batch: Any, device: torch.device):
         - :class:`torch.device`
     """
 
-    def batch_to(data):
+    def batch_to(data: Any) -> Any:
         # try to move torchtext data first
         if _TORCHTEXT_AVAILABLE and isinstance(data, Batch):
 
@@ -269,14 +275,14 @@ def move_data_to_device(batch: Any, device: torch.device):
     return apply_to_collection(batch, dtype=dtype, function=batch_to)
 
 
-def convert_to_tensors(data: Any, device: torch.device) -> Any:
+def convert_to_tensors(data: Any, device: Union[str, torch.device]) -> Any:
     if device is None:
         raise MisconfigurationException("`torch.device` should be provided.")
 
     for src_dtype, conversion_func in CONVERSION_DTYPES:
         data = apply_to_collection(data, src_dtype, conversion_func, device=device)
 
-    def _move_to_device_and_make_contiguous(t: torch.Tensor, device: torch.device) -> torch.Tensor:
+    def _move_to_device_and_make_contiguous(t: torch.Tensor, device: Union[str, torch.device]) -> torch.Tensor:
         return t.to(device).contiguous()
 
     data = apply_to_collection(data, torch.Tensor, _move_to_device_and_make_contiguous, device=device)