test_config.yml

model_path: PATH_TO_BEST_CHECKPOINT
model:
  name: UNet2D
  in_channels: 1
  out_channels: 1
  # use Groupnorm instead of Batchnorm for DSB; Batchnorm introduces artifacts around nuclei due to the difference
  # in intensity distribution between images with large and small cells
  layer_order: gcr
  num_groups: 8
  f_maps: [32, 64, 128]
  final_sigmoid: true
  is_segmentation: true
predictor:
  # standard in memory predictor
  name: 'DSB2018Predictor'
loaders:
  # dataset class
  dataset: DSB2018Dataset
  # save predictions to output_dir
  output_dir: '/home/adrian/Datasets/DSB2018/test/predictions'
  # batch dimension; if number of GPUs is N > 1, then a batch_size of N * batch_size will automatically be taken for DataParallel
  batch_size: 1
  # how many subprocesses to use for data loading
  num_workers: 8
  # test loaders configuration
  test:
    file_paths:
      - '/home/adrian/Datasets/DSB2018/test'
    transformer:
      raw:
        - name: Standardize
        - name: ToTensor
          expand_dims: true