D_full_nnunet_process.sh

# Paths:
export nnUNet_raw="./data/nnUNet_raw_data"
export nnUNet_preprocessed="./data/nnUNet_preprocessed_data"
export nnUNet_results="./nnUNet_results"

# Preprocess dataset
nnUNetv2_plan_and_preprocess -d 100 --verify_dataset_integrity

# Train folds (we could also use "all" to train all splits):
dataset=100
conf=3d_fullres
trainer=nnUNetTrainerExtremeOversamplingEarlyStoppingLowLR
nnUNetv2_train $dataset $conf 0 -device cuda -tr $trainer --npz
nnUNetv2_train $dataset $conf 1 -device cuda -tr $trainer --npz
nnUNetv2_train $dataset $conf 2 -device cuda -tr $trainer --npz
nnUNetv2_train $dataset $conf 3 -device cuda -tr $trainer --npz
nnUNetv2_train $dataset $conf 4 -device cuda -tr $trainer --npz

# Find best configuration:
nnUNetv2_find_best_configuration $dataset -c $conf -tr $trainer

# Predict
INPUT_FOLDER=/home/ander/PycharmProjects/nnunet-ms-segmentation/data/nnUNet_raw_data/Dataset100_MSSEG/imagesTs
OUTPUT_FOLDER=/home/ander/PycharmProjects/nnunet-ms-segmentation/nnUNet_test_results/Dataset100_MSSEG/nnUNetTrainerExtremeOversamplingEarlyStoppingLowLR__nnUNetPlans__3d_fullres
nnUNetv2_predict -d $dataset -i $INPUT_FOLDER -o $OUTPUT_FOLDER -f  0 1 2 3 4 -tr $trainer -c $conf -p nnUNetPlans