Thanks a lot for your reply.
Now I am stuck on the following:

  
    
    
    Question about output and label channels in semantic segmentation vision
  
  
    I use some nets,FCN8 ,SegNet for semantic segmentation .The trouble follow: 
all of the nets I used,The last layers of this net output the  feature maps is (1,22,256,256),why not (1,3,256,256)? and another question is that the labels size is (1,1,256,256),why not(1,3,256,256)? 
please help me, I a new gay
  


Essentially converting the [batch_size, nb_classes, height, width] from output with 12 classes.
predictions[‘out’]
Out[76]:
tensor([[[[-0.0465, -0.0465, -0.0465,  …, -0.0556, -0.0556, -0.0556],
[-0.0465, -0.0465, -0.0465,  …, -0.0556, -0.0556, -0.0556],
[-0.0465, -0.0465, -0.0465,  …, -0.0556, -0.0556, -0.0556],
…,
predictions[‘out’].size()
Out[77]: torch.Size([1, 12, 300, 400])