MNT: compat Python 3.9

diffeopt/group/ddmatch/action/density.py

@@ -1,4 +1,4 @@
-from typing import Callable, Any
+from typing import Callable, Any, Union
 import torch
 import numpy as np
 from ddmatch.core import (  # type: ignore
@@ -10,7 +10,7 @@
 
 # TODO: implement forward or backward in numba??
 
-def get_density_action(shape: torch.Size, compute_id: bool=False) -> Callable[[torch.Tensor, torch.Tensor | ForwardDiffeo], torch.Tensor]:
+def get_density_action(shape: torch.Size, compute_id: bool=False) -> Callable[[torch.Tensor, Union[torch.Tensor, ForwardDiffeo]], torch.Tensor]:
     image = np.zeros(shape)
     compute_grad = generate_optimized_image_gradient(image)
     compute_pullback = generate_optimized_image_composition(image)
@@ -20,7 +20,7 @@ class DensityAction(torch.autograd.Function):
         """
 
         @staticmethod
-        def forward(ctx: Any, x: torch.Tensor, g: torch.Tensor | ForwardDiffeo) -> torch.Tensor:
+        def forward(ctx: Any, x: torch.Tensor, g: Union[torch.Tensor, ForwardDiffeo]) -> torch.Tensor:
             ctx.save_for_backward(x)
             if isinstance(g, ForwardDiffeo):
                 torch_data = g.forward

diffeopt/group/ddmatch/action/function.py

@@ -1,4 +1,4 @@
-from typing import Any, Callable
+from typing import Any, Callable, Union, Optional
 import torch
 import numpy as np
 
@@ -8,7 +8,7 @@
 
 from ..group import DiffeoGroup
 
-def get_composition_action(shape: torch.Size, compute_id:bool=False) -> Callable[[torch.Tensor, torch.Tensor | Diffeo], torch.Tensor]:
+def get_composition_action(shape: torch.Size, compute_id:bool=False) -> Callable[[torch.Tensor, Union[torch.Tensor, Diffeo]], torch.Tensor]:
     """
     compute_id: right composition with the identity
     is the identity map, but it takes time to compute.
@@ -32,7 +32,7 @@ class CompositionAction(torch.autograd.Function):
         and its derivative wrt g.
         """
         @staticmethod
-        def forward(ctx: Any, q: torch.Tensor, g: torch.Tensor | Diffeo) -> torch.Tensor:
+        def forward(ctx: Any, q: torch.Tensor, g: Union[torch.Tensor, Diffeo]) -> torch.Tensor:
             if isinstance(g, Diffeo):
                 torch_data = g.forward
                 to_save = g.backward
@@ -52,7 +52,7 @@ def forward(ctx: Any, q: torch.Tensor, g: torch.Tensor | Diffeo) -> torch.Tensor
             return res
 
         @staticmethod
-        def backward(ctx: Any, grad_output: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor | None]:  # type: ignore[override]
+        def backward(ctx: Any, grad_output: torch.Tensor) -> tuple[torch.Tensor, Optional[torch.Tensor]]:  # type: ignore[override]
             """
             This is the adjoint of the derivative only
             if g was the identity.

diffeopt/group/ddmatch/representation.py

@@ -1,4 +1,4 @@
-from typing import Callable
+from typing import Callable, Union
 import torch
 
 from ..representation import Representation
@@ -7,11 +7,11 @@
 from .action.function import get_composition_action
 
 class FunctionRepresentation(Representation):
-    def get_representation(self, group: BaseDiffeoGroup) -> Callable[[torch.Tensor, torch.Tensor | Diffeo], torch.Tensor]:
+    def get_representation(self, group: BaseDiffeoGroup) -> Callable[[torch.Tensor, Union[torch.Tensor, Diffeo]], torch.Tensor]:
         return get_composition_action(group.shape)
 
 from .action.density import get_density_action
 
 class DensityRepresentation(Representation):
-    def get_representation(self, group: BaseDiffeoGroup) -> Callable[[torch.Tensor, torch.Tensor | Diffeo], torch.Tensor]:
+    def get_representation(self, group: BaseDiffeoGroup) -> Callable[[torch.Tensor, Union[torch.Tensor, Diffeo]], torch.Tensor]:
         return get_density_action(group.shape)

diffeopt/group/representation.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Callable
+from typing import Callable, Union
 from .deformation import Deformation
 import torch
 from torch.nn.parameter import Parameter
@@ -39,5 +39,5 @@ def forward(self, I: torch.Tensor) -> torch.Tensor:
         return self.representation(self.representation(I, self.perturbation.base.deformation), self.perturbation)
 
     @abstractmethod
-    def get_representation(self, group: BaseDiffeoGroup) -> Callable[[torch.Tensor, torch.Tensor | Diffeo], torch.Tensor]:
+    def get_representation(self, group: BaseDiffeoGroup) -> Callable[[torch.Tensor, Union[torch.Tensor, Diffeo]], torch.Tensor]:
         pass

diffeopt/optim.py

@@ -1,4 +1,4 @@
-from typing import Callable, Any
+from typing import Callable, Any, Optional, Union
 from abc import ABC, abstractmethod
 import torch
 from .group.representation import Perturbation
@@ -8,8 +8,8 @@
 class DiffeoOptimizer(Optimizer, ABC):
 
     @torch.no_grad()
-    def step(self, closure:Callable[[], float] | None = None) -> float | None:  # type: ignore[override]
-        loss: float | None = None
+    def step(self, closure:Optional[Callable[[], float]] = None) -> Optional[float]:  # type: ignore[override]
+        loss: Optional[float] = None
         if closure is not None:
             with torch.enable_grad():
                 loss = closure()