I am CSE student, doing my Minor Project in PyTorch on Segmentation of Organ At Risk using CT Scans and 3D Unet.
I am very new to PyTorch and Deep Learning in general. I have read first 8 chapters of the book “Deep Learning with PyTorch” to learn about PyTorch. I have created a demo jupyter notebook for my project but it has some errors due to which the GPU runs out of memory.
Context of Project In Brief:
- Takes data from GDrive.
- Creates file paths for the volumes and labels
- Created two CustomDatasets to handle 3D Volumes and its Patches
- Implemented 3D Unet From Scratch.
- Created some Metric and Losses functions (a bit of doubt here on which losses and metrics to use for 3D segmentation)
- After this the Training Loop
I will add MarkDown of these to file for better visibility
I have a few questions :
- Is my approach good enough?
- Is my 3D Unet implementation correct?
- What losses and metrics should I consider for 3D segmentation?
- Is my training loop correct?
- I am running out of GPU? can you please suggest some code changes to tackle it?
- How can I improve my code
Link to my Jupyter Notebook Here.