for unsupervised anomaly detection at pixel level, train dataset should contain only normal data and test data should include both normal and anomaly data with their masks.

* Train--> Normal

* Test:
   - Normal
   - Not normal

* ground_truth of test data 

How can I define a custom dataset for that and process it for training and evaluation?
Can I define a dataloader for train images and a custom dataset for test (images+ground_truth)?