Record performance metrics of each trained model

[ungi]

• Implement timing of final model in train scrip
• Implement timing in the Slicer sequence segmentation module.

We should keep in mind that most of our models will be deployed in real time. A typical ultrasound machine produces 15-25 frames/second. At the end of training script, we should add a loop that feeds a batch of (or e.g. 100) image frames to the trained model and computes the prediction (inference mode, no augmentation). We should time this and save the average, min, max times /frame.

The goal is around 20 ms inference time, to leave time in the user application for resizing images and do some UI rendering after each frame. We can save trained models with higher inference time for experimentation. But if we see much higher numbers, that should tell us to look at smaller models.

Additionally, we should check time/frame when using the trained model in Slicer, so we have an idea how much overhead it takes to also render images at the same time.