action log on basic unet - needs update with ignite

action_log.txt

@@ -0,0 +1,30 @@
+1. Created a simple notebook to debug the data reading in MONAI
+/mnt/data/mranzini/Desktop/GIFT-Surg/FBS_Monai/basic_unet_monai/notebooks/data_reader_debug.ipynb
+This extracts the filenames from the splitting lists used for Retraining_with_expanded dataset and then tests the application of Resizing + Random Rotation and Random Flipping as Data Augmentation
+
+2. Prepared a basic unet_training.pycode
+/mnt/data/mranzini/Desktop/GIFT-Surg/FBS_Monai/basic_unet_monai/python_code/unet_training.py
+This is based on the 3D unet segmentation examples available  in MONAI. Changes had to be made to be able to extract 2D patches from 3D images (e.g. creation of a Squeeze transform, modification of sliding window inference). Originally, the training loop was defined as plain pytorch, as it didn't seem possible to perform whole volume validation with ignite. I actually was wrong, it is possible, so eventually it would be good to refactor the code with ignite --> see segmentation_3D_ignite/unet_evaluation_dict.py
+
+3. Defined a training config file with basic inputs
+/mnt/data/mranzini/Desktop/GIFT-Surg/FBS_Monai/basic_unet_monai/config/basic_unet_config.yml
+
+4. Ran training (using monai environment)
+NOTE: I uninstalled monai (pip uninstall monai) as I want to link the source code myself so I can keep it updated instead of waiting for the pip install release to be updated. 
+This is achieved by adding:
+sys.path.append("/mnt/data/mranzini/Desktop/GIFT-Surg/FBS_Monai/MONAI")
+To run the training:
+python /mnt/data/mranzini/Desktop/GIFT-Surg/FBS_Monai/basic_unet_monai/python_code/unet_training.py --config /mnt/data/mranzini/Desktop/GIFT-Surg/FBS_Monai/basic_unet_monai/config/basic_unet_config.yml
+
+5. Prepared inference scripts:
+5a. Generated the lists for inference using:
+/mnt/data/mranzini/Desktop/GIFT-Surg/Retraining_with_expanded_dataset/bash/01b_list_inference_files.sh
+5b. Prepared the inference code
+python /mnt/data/mranzini/Desktop/GIFT-Surg/FBS_Monai/basic_unet_monai/python_code/unet_testing.py
+It pretty much follows the validation code, but I had to apply the resize within the inference loop, instead of initial transform, otherwise it is not possible to map the images back to their original size
+5c. Added the same post-processing as Guotai's network (i.e. binary closing and get largest component) 
+5d. Prepared the config file for inference
+/mnt/data/mranzini/Desktop/GIFT-Surg/FBS_Monai/basic_unet_monai/config/basic_unet_config_inference.yml
+
+6. Ran inference
+python /mnt/data/mranzini/Desktop/GIFT-Surg/FBS_Monai/basic_unet_monai/python_code/unet_testing.py --config /mnt/data/mranzini/Desktop/GIFT-Surg/FBS_Monai/basic_unet_monai/config/basic_unet_inference_config.yml