default.yaml

# modeldir is ./models/{experiment_name}/{run}
experiment_name: test
run: test


# rootdir of the data, this path is appended to the file identifier in the segmentation decatlon formatted json file
data_root: ./data/pddca
# segmentation decatlon formatted dataset description
dataset_desc: ./config/data/task_HaN.json

# workers used for data loading
num_workers: 6
val_num_workers: 6
ds_cache_rate: 1.0

# epoch validation interval
val_interval: 20
num_val_batches: 25


# continues training if a checkpoint exists
continue_training: True
# if continue_training is true and specific checkpoint file can be loaded by giving its name
# during inference, also this checkpoint file is loaded if it exists
checkpoint_file: None




# training device, can also be changed by cudas environment variables
device: 0

# num epochs
epochs: 1000
# num batches by epoch
num_epoch_batches: 125

# use automated mixed precision (tested but does not seem to save any memory)
amp: False

# deterministic training to reproduce results
determinism: True
determinism_seed: 0

# possible multi_gpu option but not tested (debug rank is likely indexing the logging machine)
multi_gpu: False
local_debug_rank: 0

inference_slw_overlap: 0.5
inference_slw_batch_size: 4
# mode options constant/gaussian
inference_slw_mode: gaussian

# final batch size is batch_size*num_samples
batch_size: 1
num_samples: 2

# use the foreground oversampling of monai which is probabilistic, nnU-Net samples according to the given ratio
# which ensures that there are always positive samples within a patch, probably a workaround for missing classes
probabilistic_pos_neg: True
pos_sample_num: 1
neg_sample_num: 2
# list of labels to be set as foreground for the foreground oversampling
labels : []

# for available options see train.get_loss.avail_losses
loss: ce_dice
lr: 1e-2
lr_decay: True


network: dynunet
dimension: 3

patch_size: [192, 160, 56]

spacing: [0.98000002, 0.98000002, 2.49962478]

# option to force isotrop resampling, nnU-Net chooses to resample 2D slices
# when the ration between max(spacing)/min(spacing) is to large
force_isotropy: False
# save softmax outputs, used to generate the train-test time violin plot of the paper
save_prob_maps: False

# remove background before sampling
crop_foreground: True
clip: [-170, 1680]
#mean and standard deviation used to normalize the data
norm: [324.7904, 505.2889]