[pre-commit.ci] auto fixes from pre-commit.com hooks

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monai/networks/blocks/head_controller.py

@@ -19,12 +19,12 @@ 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,
@@ -113,7 +113,7 @@ def forward(self, x, out, logits_options=None):
 
             params = self.controller(x_cond)
             params.squeeze_(-1).squeeze_(-1).squeeze_(-1)
-            
+
             head_inputs = self.precls_conv(out[i].unsqueeze(0))
             head_inputs = head_inputs.repeat(self.class_num,1,1,1,1)
             N, _, D, H, W = head_inputs.size()
@@ -122,9 +122,6 @@ def forward(self, x, out, logits_options=None):
 
             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

@@ -1,28 +1,24 @@
-from typing import Sequence, Tuple, Type, Union
+from typing import Sequence, Tuple, Union
 
-import numpy as np
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from torch.nn import LayerNorm
 from monai.networks.blocks.text_enbedding import TextEncoder
 from monai.networks.blocks.head_controller import HeadController
 from monai.networks.nets import SwinUNETR
 
 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
     <https://arxiv.org/abs/2201.01266>" and
-    
+
     "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,
@@ -38,8 +34,8 @@ 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)
         enc0 = self.encoder1(x_in)
@@ -53,23 +49,23 @@ def forward(self, x_in):
         dec1 = self.decoder3(dec2, enc2)
         dec0 = self.decoder2(dec1, enc1)
         out = self.decoder1(dec0, enc0)
-        
+
         return dec4, out
 
 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=768, 
-        text_dim=512, 
-        hidden_size=256, 
-        backbone = 'swinunetr', 
+        self,
+        img_size,
+        in_channels,
+        out_channels,
+        bottleneck_size=768,
+        text_dim=512,
+        hidden_size=256,
+        backbone = 'swinunetr',
         encoding = 'clip_embedding'
     ):
         super().__init__()
@@ -86,7 +82,7 @@ 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
 
         # text encoder
@@ -118,7 +114,7 @@ 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(backbone))
+            raise Exception(f'{backbone} backbone is not implemented, please add your own')
 
     def encoding_task(self, task_id):
         N = task_id.shape[0]