inference_vol_classic_example.yaml

mode: uncertainty_map # This new mode indicate the uncertainity extraction parameters for the extraction in the  pred_slice2vol: section of this file.

data:
  inference_input:
    dir: /path/to/the/input/dir
    data_type: .tiff,.tif
    reader_params:
      dimension_order_out: "CZYX"
      T: 0
  inference_output:
    path:  /path/to/the/output/dir
  preprocess:
    # # Padding smaller images               
    # - module_name: monai.transforms
    #   func_name: SpatialPad
    #   params:
    #     spatial_size: [0,300,300] 
    #     mode: "constant"
    #  #Activate this if the image sape is not divisible by 2^n = k  n number of reducction 2 in channels AttentionUnet ([1, 2, 2, 2, 2] ->2^4 = 16 =k) 
    # - module_name: monai.transforms
    #   func_name: DivisiblePad
    #   params:
    #     k: [1, k, k]
    - module_name: monai.transforms
      func_name: NormalizeIntensity
      params:
        channel_wise: True
  # This will apply slice by slice independent of the volume postpross generate_classmap will extract segmentation for logits 
  postprocess:
    - module_name: mmv_im2im.postprocessing.basic_collection
      func_name: generate_classmap
    # # use this to recover your original image size withot pad here (x,y) it's your original size
    # - module_name: monai.transforms
    #   func_name: CenterSpatialCrop
    #   params:
    #     roi_size: # depends on image shape [x,y], [0,x,y], [1,x,y]

#classic model configuration
model:
  framework: FCN
  net:
    module_name: monai.networks.nets
    func_name: AttentionUnet
    params:
      in_channels: 2 # nuber of channels in the input IM
      out_channels: 3 # one per class
      spatial_dims: 2 # 2d o 3d for convolutions
      channels: [32, 64, 128, 256, 512]
      strides: [1, 2, 2, 2, 2]
      dropout: 0.2
    # Parameter related to the uncertainty map generator and volumetric prediction slice by slice.
    pred_slice2vol:
      uncertainity_map: False  # True generate volume segmentation + uncertainity map / False : Just volume segmentation.
      multi_pred_mode: single # Prediction mode:  single -> Normal way  max/var/mean -> Take advantage of the n_samples and apply the operation to generate a final prediction.  
      max_proj: True #Make Prediction over the max projection on chanels

      # Options for volumes postproces (changes are not reflected in the uncertainity map just in the predictions).
      n_class_correction: 3
      pixel_dim: [1,1,1] # Physical pixel sizes (Z,Y,X) to take into account 'auto' -> try to take from the metadata 
      remove_object_size : [50,30]  # Removes small, isolated regions in the segmentation output. It requires the [minimum size] in pixels an object must have to be kept. 
      hole_size_threshold: [85,90] # Filling holes within segmented objects. It requires the [maximum area] in pixels a hole must have to be filled. 
      min_thickness_list: [1000,2] # Rreduce the thickness of the objects, to a minimal [pixel size] while ensuring that the connectivity and general shape of the original object are maintained. 
      perycites_correction: True 

      # Options for uncertainty map
      n_samples: 1 # Samples taken for the uncertainty computing and multiprediction option 
      # compute_mode: mutual_inf # Type of uncertainty computation variance /mutual_inf(bayesian model entropy)/entropy(total epistemic+model)/prob_inv(1-prob).
      # pertubations: ['gauss_noise','impulse_noise','speckle_noise','color_jitter','shift','rotation','pixel_dropout'] # Pertubations used for a pixel Monte Carlo Dropout (MCDO).  
      # estabilizer: True # If sets true apply sqrt to adjust values in range of 0.0x 
      # relative_MI: True   # If True -> Relative  in [0,1] range. If False -> Absolute theorical in range  [0,ln(c)] c=number of classes.
      # var_reductor: True  # If true take min uncertainty per class. If False generate uncertainity map per class.
      # trunc: 4 # Number of digits to take into accoun after the float point for the uncertainity_map (trunc = 4 -> 0.xxxx) deafult 4 if you want keep the whole float representation set as False.
      # threshold: 0.5 # Threshold for the uncertainty map  Uncer_map < threshold = 0  (turn 0 every value under de threshold). 
      # border_correction: [2,1] # Number of pixels [x,y] -> [top/dowm,left/right] to take into acount in the border correction.
  checkpoint:  /path/to/the/ckpt/file/with/model/weights
  model_extra:
    cpu_only: False
trainer:
  verbose: True
  params:
    gpus: 1
    precision: 16