auto updates (#7105)

Signed-off-by: monai-bot <[email protected]>

Signed-off-by: monai-bot <[email protected]>

monai/apps/auto3dseg/bundle_gen.py

@@ -47,7 +47,6 @@
 logger = get_logger(module_name=__name__)
 ALGO_HASH = MONAIEnvVars.algo_hash()
 
-
 __all__ = ["BundleAlgo", "BundleGen"]
 
 

monai/apps/detection/networks/retinanet_network.py

@@ -88,8 +88,8 @@ def __init__(
 
         for layer in self.conv.children():
             if isinstance(layer, conv_type):  # type: ignore
-                torch.nn.init.normal_(layer.weight, std=0.01)  # type: ignore
-                torch.nn.init.constant_(layer.bias, 0)  # type: ignore
+                torch.nn.init.normal_(layer.weight, std=0.01)
+                torch.nn.init.constant_(layer.bias, 0)
 
         self.cls_logits = conv_type(in_channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1)
         torch.nn.init.normal_(self.cls_logits.weight, std=0.01)
@@ -167,8 +167,8 @@ def __init__(self, in_channels: int, num_anchors: int, spatial_dims: int):
 
         for layer in self.conv.children():
             if isinstance(layer, conv_type):  # type: ignore
-                torch.nn.init.normal_(layer.weight, std=0.01)  # type: ignore
-                torch.nn.init.zeros_(layer.bias)  # type: ignore
+                torch.nn.init.normal_(layer.weight, std=0.01)
+                torch.nn.init.zeros_(layer.bias)
 
     def forward(self, x: list[Tensor]) -> list[Tensor]:
         """
@@ -297,7 +297,7 @@ def __init__(
             )
         self.feature_extractor = feature_extractor
 
-        self.feature_map_channels: int = self.feature_extractor.out_channels  # type: ignore[assignment]
+        self.feature_map_channels: int = self.feature_extractor.out_channels
         self.num_anchors = num_anchors
         self.classification_head = RetinaNetClassificationHead(
             self.feature_map_channels, self.num_anchors, self.num_classes, spatial_dims=self.spatial_dims

monai/apps/reconstruction/networks/blocks/varnetblock.py

@@ -55,7 +55,7 @@ def soft_dc(self, x: Tensor, ref_kspace: Tensor, mask: Tensor) -> Tensor:
         Returns:
             Output of DC block with the same shape as x
         """
-        return torch.where(mask, x - ref_kspace, self.zeros) * self.dc_weight  # type: ignore
+        return torch.where(mask, x - ref_kspace, self.zeros) * self.dc_weight
 
     def forward(self, current_kspace: Tensor, ref_kspace: Tensor, mask: Tensor, sens_maps: Tensor) -> Tensor:
         """

monai/losses/cldice.py

@@ -29,12 +29,12 @@ def soft_erode(img: torch.Tensor) -> torch.Tensor:  # type: ignore
     if len(img.shape) == 4:
         p1 = -(F.max_pool2d(-img, (3, 1), (1, 1), (1, 0)))
         p2 = -(F.max_pool2d(-img, (1, 3), (1, 1), (0, 1)))
-        return torch.min(p1, p2)  # type: ignore
+        return torch.min(p1, p2)
     elif len(img.shape) == 5:
         p1 = -(F.max_pool3d(-img, (3, 1, 1), (1, 1, 1), (1, 0, 0)))
         p2 = -(F.max_pool3d(-img, (1, 3, 1), (1, 1, 1), (0, 1, 0)))
         p3 = -(F.max_pool3d(-img, (1, 1, 3), (1, 1, 1), (0, 0, 1)))
-        return torch.min(torch.min(p1, p2), p3)  # type: ignore
+        return torch.min(torch.min(p1, p2), p3)
 
 
 def soft_dilate(img: torch.Tensor) -> torch.Tensor:  # type: ignore
@@ -48,9 +48,9 @@ def soft_dilate(img: torch.Tensor) -> torch.Tensor:  # type: ignore
         https://github.com/jocpae/clDice/blob/master/cldice_loss/pytorch/soft_skeleton.py#L18
     """
     if len(img.shape) == 4:
-        return F.max_pool2d(img, (3, 3), (1, 1), (1, 1))  # type: ignore
+        return F.max_pool2d(img, (3, 3), (1, 1), (1, 1))
     elif len(img.shape) == 5:
-        return F.max_pool3d(img, (3, 3, 3), (1, 1, 1), (1, 1, 1))  # type: ignore
+        return F.max_pool3d(img, (3, 3, 3), (1, 1, 1), (1, 1, 1))
 
 
 def soft_open(img: torch.Tensor) -> torch.Tensor:

monai/networks/blocks/feature_pyramid_network.py

@@ -194,8 +194,8 @@ def __init__(
         conv_type_: type[nn.Module] = Conv[Conv.CONV, spatial_dims]
         for m in self.modules():
             if isinstance(m, conv_type_):
-                nn.init.kaiming_uniform_(m.weight, a=1)  # type: ignore
-                nn.init.constant_(m.bias, 0.0)  # type: ignore
+                nn.init.kaiming_uniform_(m.weight, a=1)
+                nn.init.constant_(m.bias, 0.0)
 
         if extra_blocks is not None:
             if not isinstance(extra_blocks, ExtraFPNBlock):

monai/networks/nets/swin_unetr.py

@@ -519,7 +519,7 @@ def forward(self, x, mask):
         q = q * self.scale
         attn = q @ k.transpose(-2, -1)
         relative_position_bias = self.relative_position_bias_table[
-            self.relative_position_index.clone()[:n, :n].reshape(-1)  # type: ignore
+            self.relative_position_index.clone()[:n, :n].reshape(-1)
         ].reshape(n, n, -1)
         relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
         attn = attn + relative_position_bias.unsqueeze(0)
@@ -678,7 +678,7 @@ def load_from(self, weights, n_block, layer):
             self.norm1.weight.copy_(weights["state_dict"][root + block_names[0]])
             self.norm1.bias.copy_(weights["state_dict"][root + block_names[1]])
             self.attn.relative_position_bias_table.copy_(weights["state_dict"][root + block_names[2]])
-            self.attn.relative_position_index.copy_(weights["state_dict"][root + block_names[3]])  # type: ignore
+            self.attn.relative_position_index.copy_(weights["state_dict"][root + block_names[3]])
             self.attn.qkv.weight.copy_(weights["state_dict"][root + block_names[4]])
             self.attn.qkv.bias.copy_(weights["state_dict"][root + block_names[5]])
             self.attn.proj.weight.copy_(weights["state_dict"][root + block_names[6]])

monai/networks/nets/transchex.py

@@ -46,7 +46,7 @@ def __init__(self, *inputs, **kwargs) -> None:
 
     def init_bert_weights(self, module):
         if isinstance(module, (nn.Linear, nn.Embedding)):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)  # type: ignore
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
         elif isinstance(module, torch.nn.LayerNorm):
             module.bias.data.zero_()
             module.weight.data.fill_(1.0)