semseg | commonly used semantic segmentation architecture structure | Machine Learning library

I am trying to use the following CNN architecture for semantic pixel classification. The code I am using is here

However, from my understanding this type of semantic segmentation network typically should have a softmax output layer for producing the classification result.

I could not find softmax used anywhere within the script. Here is the paper I am reading on this segmentation architecture. From Figure 2, I am seeing softmax being used. Hence I would like to find out why this is missing in the script. Any insight is welcome.

I have an XML of the form:

I would like to follow the Convolutional Neural Net (CNN) approach here. However, this code in github uses Pytorch, whereas I am using Keras.

I want to reproduce boxes 6,7 and 8 where pre-trained weights from VGG-16 on ImageNet is downloaded and is used to make the CNN converge faster.

In particular, there is a portion (box 8) where weights are downloaded and skipped from VGG-16 that have no counterpart in SegNet (the CNN model). In my work, I am using a CNN model called U-Net instead of Segnet. The U-Net Keras code that I am using can be found here.

I am new to Keras and would appreciate any insight in Keras code on how I can go about downloading and skipping the VGG weights that have no counterpart with my U-Netmodel.

I am new to Python and I am trying to use the function 'one_hot_to_label_batch' which can be found on line 115 from this website.

However, directly above this function there is a '@expand_dims'. This is the first time I have encountered this. I know 'expand_dims' is within Numpy but I do not know why it is defined as '@expand_dims' here. Any clarification would be appreciated.

I am using code from this website

There is a function called 'label_to_one_hot_batch' within the file `isprs.py.

In my own python script in the main directory folder (i.e., the semseg folder), I am trying to import this function as follows: