[basics]

train_flag: false

compute_patches: false

resume: false

test_flag: true

uncertainty_flag: true

plot: false

tensorboard_logs: tensorboard_logs/

[uncertainty]

n_iterations: 20

# uncertainty_type: ttd

uncertainty_type: tta

use_dropout: false

[model]

model_path_local: /Users/lauramora/Documents/MASTER/TFM/Code/BrainTumorSegmentation/results/checkpoints/last_models/

model_path_server: /mnt/gpid07/users/laura.mora/results/checkpoints/

# newer checks!

# checkpoint: last_models/checkpoint_epoch_349_val_loss_0.33079595190204986_dice_0.6692040464649461.pth

# checkpoint: model_1598640005/checkpoint_epoch_168_val_loss_0.20105469390137554_dice_0.7989453060986245.pth

# checkpoint: model_1598639885/checkpoint_epoch_198_val_loss_0.19342842820572526_dice_0.8065715717942747.pth

checkpoint: model_1598640035/checkpoint_epoch_142_val_loss_0.21437616135976087_dice_0.7856238380039416.pth

# checkpoint: model_1598550861/checkpoint_epoch_215_val_loss_0.2378825504485875_dice_0.7621174487349105.pth

# checkpoint: model_1598651693/checkpoint_epoch_297_val_loss_0.3553243059001557_dice_0.644675692108026.pth

# checkpoint: model_1598651693/checkpoint_epoch_316_val_loss_0.33101850666411936_dice_0.6689814933358806.pth

loss: gdl

eval_regions: false

init_features_maps: 32

n_epochs: 100

# network: 3dunet

network: 3dunet_residual

# network: vnet_asymm

# unet

unet_order: crg

# cli  -  conv + LeakyReLU + instancenorm

# vnet asymm

non_linearity: relu

kernel_size: 3

padding: 1

# vnet

use_elu: true

# optimizer

optimizer: ADAM

learning_rate: 1e-4

weight_decay: 1e-5

## sgd only

momentum: 0.99

# scheduler LR

scheduler: true

patience: 30

## by a factor of 5

lr_decay: 0.2

[dataset]

dataset_root_path_server: /mnt/gpid07/users/laura.mora/datasets/2020/

dataset_root_path_local: /Users/lauramora/Documents/MASTER/TFM/Data/2020/

dataset_train_folder: train

dataset_val_folder: validation

dataset_test_folder: test

train_csv: brats20_data.csv

test_csv: brats20_data.csv

val_csv: brats20_val.csv

classes: 4

n_modalities: 4

# Use dataloader

batch_size: 2

lgg_only: false

# If using sampler

n_patients_per_batch: 8

n_patches: 1

# source_sampling: src.dataset.patching.centered_crop_patch

# source_sampling: src.dataset.patching.random_tumor_distribution

source_sampling: src.dataset.patching.no_patch

# sampling_method: src.dataset.patching.no_patch

sampling_method: src.dataset.patching.random_tumor_distribution

# sampling_method: src.dataset.patching.random_distribution

# sampling_method: src.dataset.patching.binary_distribution

patch_size: 64

            64

            64