colab.research.google.com
  
  
    

Google Colaboratory 7



  
  
    
    
  
  


Please find link to the colab notebook. It has been well annotated and hope you can help me!
I am implementing UNET architecture for segmenting input image into 4 classes.
Input 900 grayscale images → [900, 1, 256, 256]
Mask/Ground Truth               → [900, 1, 256, 256]
Each pixel in the mask has one of the following values - 0,1,2,3 (4 classes)
I understand the output shape of my model should be:
[batch_size, nb_classes, height ,width].
In my case model outputs tensor of size = [batch_size, 4, 256, 256]
After training using CrossEntropy Loss, I try to make predictions using: 
Prediction for a single image y_pred has a shape [4,256,256] whereas my mask shape is [1, 256, 256]
How can I compare my predictions and ground truth when the shapes are mismatched.
Ideally my prediction should be [1,256,256] with 4 unique values(nb_class=4)
I read that I am not supposed to use torch.sigmoid() with nn.CrossEntropy loss. What is the right way to get predictions?
Also want to clarify if I have to one hot encode or map my masks.