save-tensors - Save reconstruction outputs

This tool allows to save the output tensors of a whole data group, associated with the tensor corresponding to their input. This can be useful for the reconstruction task, for which the user may want to perform extra analyses directly on the images reconstructed by a trained network, or simply visualize them for a qualitative check.

Prerequisites

Please check which preprocessing needs to be performed in the maps.json file of the MAPS. If it has not been performed, execute the preprocessing pipeline as well as clinicadl extract to obtain the tensor versions of the images.

Running the task

This task can be run with the following command line:

clinicadl save-tensor [OPTIONS] INPUT_MAPS_DIRECTORY DATA_GROUP

where:

• INPUT_MAPS_DIRECTORY (Path) is a path to the MAPS folder containing the model which will be interpreted.
• DATA_GROUP (str) is a prefix to name the files resulting from the interpretation task.

data group consistency

For ClinicaDL, a data group is linked to a list of participants / sessions and a CAPS directory. When performing a prediction, interpretation or tensor serialization the user must give a data group. If this data group does not exist, the user MUST give a caps_path and a tsv_path. If this data group already exists, the user MUST not give any caps_path or tsv_path, or set overwrite to True.

Optional arguments:

• Computational resources
  • --gpu / --no-gpu (bool) Uses GPU acceleration or not. Default behavior is to try to use a GPU. If not available an error is raised. Use the option --no-gpu if running in CPU.
  • --n_proc (int) is the number of workers used by the DataLoader. Default: 2.
  • --batch_size (int) is the size of the batch used in the DataLoader. Default: 2.
• Model selection
  • --selection_metrics (List[str]) is a list of metrics to find the best models to evaluate. Default will predict the results for best model based on the loss only.
• Data management
  • --participants_tsv (Path) is a path to a directory containing one TSV file per diagnosis (see output tree of getlabels). Default will use the same participants as those used during the training task.
  • --caps_directory (Path) is the path to a CAPS hierarchy. Default will use the same CAPS as during the training task.
  • --multi_cohort (bool) is a flag indicated that multi-cohort classification is performed. In this case, caps_directory and tsv_path must be paths to TSV files.
  • --diagnoses (List[str]) if tsv_file is a split directory, then will only load the labels wanted. Default will look for the same labels used during the training task.

Outputs

Results are stored in the MAPS of path maps_directory, according to the following file system:

<maps_directory>
    ├── split-0  
    ├── ...  
    └── split-<i>
        └── best-<metric>
                └── <data_group>
                    └── tensors
                        ├── <participant_id>_<session_id>_{image|patch|roi|slice}-<X>_input.pt
                        └── <participant_id>_<session_id>_{image|patch|roi|slice}-<X>_output.pt

For each participant_id, session_id and index of the part of the image (X), the input and the output tensors are saved in.