config.yaml

data_path: ./data # path to the directory of your tiff images
output_path: ./outputs # path where you want to save workflow outputs, i.e. trained models, predicted images etc.
dataset: acquisition_190821  # the name of your dataset, should correspond to directory name within your data_path
path_model_dir: ["./saved_models/pretrained_model"] # the path to the pre-trained model
signal_channel: 3 # the channel id of the brightfield image
target_channel: 2 # the channel id of the infection image - set to None if only going to use first part of workflow and you don't have a dataset with infection marker
dapi_channel: 0 # the channel id of the DAPI image - set to None if only going to use first part of workflow and you don't have a dataset with DAPI marker
train_size: 0.8 # defines train-test split
kfolds: 5 # number of folds for cross validation - set to 1 if you do not wish to apply cross-fold validation
val_size: 0 # specify percentage of train split to be used for validation if  cross validation is not applied
searches_per_fold: 5 # number of times to repeat a hyperparameter search for a specific k-fold 
run: '2008211533' # name of experiment

preprocessing:
  transform_signal: ['fnet.transforms.normalize', 'fnet.transforms.AdaptRange']
  transform_target: ['fnet.transforms.AdaptRange'] # set to None if there is no target in dataset

training:
  method: fine-tune # specify either "fine-tune", "from-scratch", "both"
  num_trials: 50 # number of searches for best hyperparameter configuration
  n_iter: 16000
  augmentations: True
  
#hyperparameters:
lr: 0.0004
batch_size: 16
buffer_size: 20 # needs to be at least as big as train set(probably quite smaller)
buffer_switch_frequency: 285
dropout: 0.19
threshold_backround: 0.2
resampling_probability: 0.669
num_freeze_layers: 0 
patch_size: [256,256]
depth: 5
loss_weight: 0.7
shuffle_images: True
min_dif: 0.05
max_dif: 1

class_dataset: 'TiffDataset'
gpu_ids: 0 # set to -1 if you do not have a gpu - not recommended!
seed: 1
print_iter: 200
nn_module: 'fnet_nn_2d'
in_channels: 1
out_channels: 1
bpds_kwargs: {}
nn_kwargs: {}
module_fnet_model: fnet_model

prediction:
  no_signal: False #set to not save signal image
  no_target: False #set to not save target image
  no_prediction: True
  no_prediction_unpropped: False #set to not save unpropped prediction image
  return_score: True # set to true to save Pearson Correlation Coefficient scores for images
  n_images: -1 # put negative to take all in test csv file
  propper_kwargs: {}