Configuration

To perform experiments for training, validation or inference, a configuration file with an extension .yaml must be defined which includes all experiments parameters such as models, datasets, etc. The following is a template for the configuration file.

model:
model_name: <nn_model_path>
model_parameter_1: <nn_model_paramter_1>
model_parameter_2: <nn_model_paramter_2>
...

additional_models:
  sensitivity_model:
    model_name: <nn_sensitivity_model_path>
    ...

physics:
  forward_operator: fft2(centered=False)
  backward_operator: ifft2(centered=False)
  ...

training:
  datasets:
  - name: Dataset1
    lists:
    - <path_to_list_1_for_Dataset1>
    - <path_to_list_2_for_Dataset1>
    transforms:
      cropping:
          crop: <shape_or_str>
          image_center_crop: <true_or_false>
      sensitivity_map_estimation:
          estimate_sensitivity_maps: <true_or_false>
      normalization:
          scaling_key: <stringg>
      masking:
        name: MaskingFunctionName
        accelerations: [acceleration_1, accelaration_2, ...]
        ...
    ...
  - name: Dataset2
    lists:
    ...
    transforms:
      ...
      masking:
        name: MaskingFunctionName
        accelerations: [acceleration_1, accelaration_2, ...]
        ...
    ...
  optimizer: <optimizer>
  lr: <learning_rate>
  batch_size: <batch_size>
  lr_step_size: <lr_step_size>
  lr_gamma: <lr_gamma>
  lr_warmup_iter: <num_warmup_iterations>
  num_iterations: <num_iterations>
  validation_steps: <num_val_steps>
  loss:
    losses:
    - function: <fun1_as_in_model_engine>
      multiplier: <multiplier_1>
    - function: <fun2_as_in_model_engine>
      multiplier: <multiplier_2>
  checkpointer:
    checkpoint_steps: <num_checkpointer_steps>
  metrics: [<metric_1, metric_2, ...]
  ...

validation:
  datasets:
  - name: ValDataset1
    transforms:
      ...
      masking:
        ...
    text_description: <val_description_1>
    ...
  - name: ValDataset2
    transforms:
      ...
      masking:
        ...
      text_description: <val_description_2>
      ...
  - name: ...
  ...
  batch_size: <val_batch_size>
  metrics:
  - val_metric_1
  - val_metric_2
  - ...
  ...

inference:
  dataset:
    name: InferenceDataset
    lists: ...
    transforms:
      masking:
        ...
      ...
    text_description: <inference_description>
    ...
  batch_size: <batch_size>
  ...

logging:
  tensorboard:
    num_images: <num_images>

The following configuration files are accepted for each field:

• physics, training, and validation configurations: direct/config/defaults.py

• transforms configurations: direct/data/datasets_config.py

• model configurations: direct/nn/<model_name>/config.py

A list of our configuration files can be found in the projects folder.