Removed some unnecessary type hint.

open-mmlab · May 31, 2022 · 52111dc · 52111dc
1 parent 1cf5dde
commit 52111dc
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Showing 3 changed files with 15 additions and 5 deletions.
diff --git a/mmcv/ops/focal_loss.py b/mmcv/ops/focal_loss.py
@@ -96,7 +96,7 @@ def __init__(self,
                  gamma: float,
                  alpha: float,
                  weight: Optional[torch.Tensor] = None,
-                 reduction: str = 'mean') -> None:
+                 reduction: str = 'mean'):
         super().__init__()
         self.gamma = gamma
         self.alpha = alpha
@@ -209,7 +209,7 @@ def __init__(self,
                  gamma: float,
                  alpha: float,
                  weight: Optional[torch.Tensor] = None,
-                 reduction: str = 'mean') -> None:
+                 reduction: str = 'mean'):
         super().__init__()
         self.gamma = gamma
         self.alpha = alpha

diff --git a/mmcv/ops/knn.py b/mmcv/ops/knn.py
@@ -1,3 +1,5 @@
+from typing import Optional
+
 import torch
 from torch.autograd import Function
 
@@ -19,7 +21,7 @@ class KNN(Function):
     def forward(ctx,
                 k: int,
                 xyz: torch.Tensor,
-                center_xyz: torch.Tensor = None,
+                center_xyz: Optional[torch.Tensor] = None,
                 transposed: bool = False) -> torch.Tensor:
         """
         Args:

diff --git a/mmcv/ops/masked_conv.py b/mmcv/ops/masked_conv.py
@@ -16,7 +16,9 @@
 class MaskedConv2dFunction(Function):
 
     @staticmethod
-    def symbolic(g, features, mask, weight, bias, padding, stride):
+    def symbolic(g, features: torch.Tensor, mask: torch.Tensor,
+                 weight: torch.Tensor, bias: torch.Tensor, padding: int,
+                 stride: int):
         return g.op(
             'mmcv::MMCVMaskedConv2d',
             features,
@@ -27,7 +29,13 @@ def symbolic(g, features, mask, weight, bias, padding, stride):
             stride_i=stride)
 
     @staticmethod
-    def forward(ctx, features, mask, weight, bias, padding=0, stride=1):
+    def forward(ctx,
+                features: torch.Tensor,
+                mask: torch.Tensor,
+                weight: torch.Tensor,
+                bias: torch.Tensor,
+                padding: int = 0,
+                stride: int = 1) -> torch.Tensor:
         assert mask.dim() == 3 and mask.size(0) == 1
         assert features.dim() == 4 and features.size(0) == 1
         assert features.size()[2:] == mask.size()[1:]