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monai/networks/blocks/head_controller.py

@@ -18,12 +18,12 @@ class HeadController(nn.Module):
     """
     Text-based controller for segmentation outputs, the text-driven segmentor enables for optional outputs instead of
     fixed output channels. Users can choose and control the number and name of output channels from a mult-class segmentation
-    model. This can enabble incremental learning by adding new classes to a existing pre-trained model without 
+    model. This can enabble incremental learning by adding new classes to a existing pre-trained model without
     catatrophic forgetting.
-    
+
     Text-dirven segmentor, based on: "Liu et al.,
     CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection <https://arxiv.org/pdf/2301.00785.pdf>"
-    """    
+    """
     def __init__(
         self,
         out_channels: int,
@@ -45,19 +45,19 @@ def __init__(
             text_encoding: the text embedding features passed.
         """
         super().__init__()
-        
+
         self.head_hidden_size = head_hidden_size
-        self.bias_nums = [head_hidden_size] * (head_layers - 1) + [1] # defined by segmentor head's hidden size, last element of 1. 
+        self.bias_nums = [head_hidden_size] * (head_layers - 1) + [1] # defined by segmentor head's hidden size, last element of 1.
         self.weight_nums = [head_in_channels*head_hidden_size] + [head_hidden_size*head_hidden_size]*(head_layers-2) + [head_hidden_size] #first+intermediate+last layer
-        
+
         self.class_num = out_channels
         self.text_encoding = text_encoding
         # text-driven controller: connection of bottleneck feature to segmentor features, e.g., from 256(*2) to weights and bias nums
         if self.text_encoding:
             self.controller = nn.Conv3d(2*hidden_size, sum(self.weight_nums+self.bias_nums), kernel_size=1, stride=1, padding=0)
         else:
             self.controller = nn.Conv3d(hidden_size, sum(self.weight_nums+self.bias_nums), kernel_size=1, stride=1, padding=0)
-        # convolution layer of backbone output to segmentor head input size, e.g., 48 to 8 
+        # convolution layer of backbone output to segmentor head input size, e.g., 48 to 8
         self.precls_conv = nn.Sequential(
             nn.GroupNorm(16, feature_size),
             nn.ReLU(inplace=True),
@@ -106,7 +106,7 @@ def heads_forward(self, features, weights, biases, num_insts):
                 x = nn.functional.relu(x)
         return x
 
-    def forward(self, x, out, text_encoding=None, logits_options=None): 
+    def forward(self, x, out, text_encoding=None, logits_options=None):
         logits_options = range(self.class_num) if not isinstance(logits_options, list) else logits_options
         b = x.shape[0]
         logits_array = []
@@ -128,8 +128,6 @@ def forward(self, x, out, text_encoding=None, logits_options=None):
             weights, biases = self.parse_dynamic_params(params, self.head_hidden_size, self.weight_nums, self.bias_nums)
             logits = self.heads_forward(head_inputs, weights, biases, N)
             logits_array.append(logits.reshape(1, -1, D, H, W))
-        
+
         out = torch.cat(logits_array,dim=0)
         return out
-
-

monai/networks/nets/universal_model.py

@@ -14,16 +14,16 @@ class Universal_model(nn.Module):
     """
     Universal Model for organ and tumor segmentation, based on: "Liu et al.,
     CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection <https://arxiv.org/pdf/2301.00785.pdf>"
-    """ 
+    """
     def __init__(
-        self, 
-        img_size, 
-        in_channels, 
-        out_channels, 
-        bottleneck_size: int = 768, 
-        text_dim: int = 512, 
-        hidden_size: int = 256, 
-        backbone: str = 'swinunetr', 
+        self,
+        img_size,
+        in_channels,
+        out_channels,
+        bottleneck_size: int = 768,
+        text_dim: int = 512,
+        hidden_size: int = 256,
+        backbone: str = 'swinunetr',
         encoding: str = 'clip_embedding',
         logits_options: list = None,
     ):
@@ -41,7 +41,7 @@ def __init__(
                         use_checkpoint=False,
                         )
         else:
-            raise Exception('{} backbone is not implemented, please add your own'.format(backbone))
+            raise Exception(f'{backbone} backbone is not implemented, please add your own')
         self.class_num = out_channels
         self.logits_options = logits_options
         # text encoder
@@ -74,7 +74,7 @@ def load_params(self, model_dict):
             self.backbone.load_state_dict(store_dict)
             print('Use swin unetr pretrained weights')
         else:
-            raise Exception('{} backbone is not implemented, please add your own'.format(self.backbone_name))
+            raise Exception(f'{self.backbone_name} backbone is not implemented, please add your own')
 
     def forward(self, x_in):
         # get backbone feature
@@ -89,7 +89,7 @@ def forward(self, x_in):
 
 class SwinUNETR_backbone(SwinUNETR):
     """
-    Universal Model uses SwinUNETR as backbone without the segmentation head based on: 
+    Universal Model uses SwinUNETR as backbone without the segmentation head based on:
 
     "Hatamizadeh et al.,
     Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
@@ -98,8 +98,8 @@ class SwinUNETR_backbone(SwinUNETR):
     "Tang et al.,
     Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
     <https://arxiv.org/abs/2111.14791>"
-    """ 
-    def __init__(        
+    """
+    def __init__(
         self,
         img_size: Union[Sequence[int], int],
         in_channels: int,
@@ -115,7 +115,7 @@ def __init__(
         use_checkpoint: bool = False,
         spatial_dims: int = 3,
     ):
-        super(SwinUNETR_backbone, self).__init__(img_size,in_channels,out_channels,feature_size=48)
+        super().__init__(img_size,in_channels,out_channels,feature_size=48)
 
     def forward(self, x_in):
         hidden_states_out = self.swinViT(x_in, self.normalize)
@@ -132,4 +132,3 @@ def forward(self, x_in):
         out = self.decoder1(dec0, enc0)
 
         return dec4, out
-