userconf.yml

# User-defined configuration file; things here depend on where your files are etc.

# Where the RDSF is mounted - for reading the data + creating DICOMs
# The create_dicoms.py script creates these
rdsf_dir: "/home/mh19137/zebrafish_rdsf/"

# Where the DICOMs are stored - this is where the training data lives
dicom_dirs:
  - "/home/mh19137/zebrafish_jaw_segmentation/dicoms/Training set 1/"
  - "/home/mh19137/zebrafish_jaw_segmentation/dicoms/Training set 2/"
  - "/home/mh19137/zebrafish_jaw_segmentation/dicoms/Training set 3 (base of jaw)/"

# Must end in .pkl (the model will be pickled)
# The model will be saved in the model/ directory
model_path: "test.pkl"

# Which ones to use for testing and validation
# all the others will be used for testing
validation_dicoms:
 - "39"  # 7 month wt, labelled by Wahab
 - "86"  # 3 year wt, labelled by Felix

# At the moment this can only be 1-length
test_dicoms:
 - "131"  # 2.5yr het chst11 chst11 talen

# Optimiser options
optimiser: "Adam"  # Must be one of the torch.optim optimiser
learning_rate: 0.001

# Loss function options
loss: "monai.losses.TverskyLoss"
loss_options: {
  "include_background": false,
  "to_onehot_y": true,
  "alpha": 0.15,
  "beta": 0.85,
  "sigmoid": true,
}

# RNG seeds
# Note that this still doesn't guarantee reproducibility,
# since the dataloaders and probably some algorithms/other things have different sources
# of randomness
# But (hopefully) it should mean our model is initialised the same way each time
torch_seed: 0
test_train_seed: 1

# Options for the model that you might want to change
# These are sort of like meta-parameters so they're not in the model_params section but maybe they should be
device: "cuda"
window_size: "192,192,192"  # Comma-separated ZYX. Needs to be large enough to hold the whole jaw
patch_size: "160,160,160"  # Bigger holds more context, smaller is faster and allows for bigger batches
batch_size: 12
epochs: 500
lr_lambda: 0.99999  # Exponential decay factor (multiplicative with each epoch)
num_workers: 6  # Number of workers for the dataloader

# Options should be passed
transforms:
  torchio.RandomFlip:
    axes: [0, 1, 2]
    flip_probability: 0.5
  torchio.RandomAffine:
    p: 0.25
    degrees: 10
    scales: 0.2
# Other options might be
# torchio.RandomBlur(p=0.3),
# torchio.RandomBiasField(0.4, p=0.5),
# torchio.RandomNoise(0.1, 0.01, p=0.25),
# torchio.RandomGamma((-0.3, 0.3), p=0.25),
# torchio.ZNormalization(),
# torchio.RescaleIntensity(percentiles=(0.5, 99.5)),

model_params:
  model_name: "monai.networks.nets.AttentionUnet"

  # Things you probably won't need to change - I just kept them here to keep all the params in one place
  spatial_dims: 3
  n_classes: 2  # n bones + background
  in_channels: 1  # Our images are greyscale

  # Things you might want to change
  # At the moment, I can't have more than 6 layers in the model because the receptive field
  # gets halved and ends up being an odd number, and then when we upsample we get a size mismatch
  n_layers: 6
  n_initial_channels: 8
  kernel_size: 3
  stride: 2
  dropout: 0.01